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Wi-Fi is an integral part of today's Internet infrastructure, enabling a diverse range of applications and services. Prior approaches to Wi-Fi resource allocation optimized Quality of Service (QoS) metrics, which often do not accurately reflect the user's Quality of Experience (QoE). To address the gap between QoS and QoE, we introduce Maestro, an adaptive method that formulates the Wi-Fi resource allocation problem as a partially observable Markov decision process (PO-MDP) to maximize the overall system QoE and QoE fairness. Maestro estimates QoE without using any application or client data; instead, it treats them as black boxes and leverages temporal dependencies in network telemetry data. Maestro dynamically adjusts policies to handle different classes of applications and variable network conditions. Additionally, Maestro uses a simulation environment for practical training. We evaluate Maestro in an enterprise-level Wi-Fi testbed with a variety of applications, and find that Maestro achieves up to 25× and 78% improvement in QoE and fairness, respectively, compared to the widely-deployed Wi-Fi Multimedia (WMM) policy. Compared to the state-of-the-art learning approach QFlow, Maestro increases QoE by up to 69%. Unlike QFlow which requires modifications to clients, we demonstrate that Maestro improves QoE of popular over-the-top services with unseen traffic without control over clients or servers. 

In the era of 5G and beyond, dynamic Time Division Duplex (TDD) has become essential for supporting applications that demand high bandwidth and low latency. Emerging uplink-intensive use cases such as real-time video analytics, autonomous vehicles and augmented reality further complicate the balance between uplink and downlink resources. Despite their potential, TDD policies employed by current 5G networks remain underexplored. Our investigation reveals that existing TDD policies are static and predominantly downlink-focused, failing to adapt to fluctuating network demands. We introduce Wixor, a robust dynamic TDD policy adaptation system tailored for 5G and next-generation (xG) networks. It proactively adjusts the allocation of TDD resources between uplink and downlink, addressing various practical challenges. Prototyped on a programmable testbed, Wixor demonstrates substantial performance improvements across diverse applications, achieving up to 96.5% enhancement in Quality of Experience (QoE) compared to existing baselines. 

Emerging multimedia applications often use a wireless LAN (Wi-Fi) infrastructure to stream content. These Wi-Fi deployments vary vastly in terms of their system configurations. In this paper, we take a step toward characterizing the Quality of Experience (QoE) of volumetric video streaming over an enterprise-grade Wi-Fi network to: (i) understand the impact of Wi-Fi control parameters on user QoE, (ii) analyze the relation between Quality of Service (QoS) metrics of Wi-Fi networks and application QoE, and (iii) compare the QoE of volumetric video streaming to traditional 2D video applications. We find that Wi-Fi configuration parameters such as channel width, radio interface, access category, and priority queues are important for optimizing Wi-Fi networks for streaming immersive videos. 

After a rapid deployment worldwide over the past few years, 5G is expected to have reached a mature deployment stage to provide measurable improvement of network performance and user experience over its predecessors. In this study, we aim to assess 5G deployment maturity via three conditions: (1) Does 5G performance remain stable over a long time span? (2) Does 5G provide better performance than its predecessor LTE? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting a cross-sectional, year-long measurement study of 5G uplink performance. Leveraging a custom Android App, we collected 5G uplink performance measurements (of critical importance to latency-critical apps) spanning 8 major cities in 7 countries and two different continents. Our measurements show that 5G deployment in major cities appears to have matured, with no major performance improvements observed over a one-year period, but 5G does not provide consistent, superior measurable performance over LTE, especially in terms of latency, and further there exists clear uneven 5G performance across the 8 cities. Our study suggests that, while 5G deployment appears to have stagnated, it is short of delivering its promised performance and user experience gain over its predecessor. 

One of the key enhancements in the upcoming 802.11ay standard for 60 GHz WLANs is the support for simultaneous transmissions of up to 8 data streams via SU- and MU-MIMO, which has the potential to enable data rates up to 100 Gbps. However, in spite of the key role MIMO is expected to play in 802.11ay, experimental evaluation of MIMO performance in 60 GHz WLANs has been limited to date, primarily due to lack of hardware supporting MIMO transmissions at millimeter wave frequencies. In this work, we fill this gap by conducting the first large-scale experimental evaluation of SU- and MU-MIMO performance in 60 GHz WLANs. Unlike previous studies, our study involves multiple environments with very different multipath characteristics. We analyze the performance in each environment, identify the factors that affect it, and compare it against the performance of SISO. Further, we seek to identify factors that can guide beam and user selection to limit the (often prohibitive in practice) overhead of exhaustive search. Finally, we propose two heuristics that perform both user and beam selection with low overhead, and show that they perform close to an Oracle solution and outperform previously proposed approaches in both static and mobile scenarios, regardless of the environment and number of users. 

The well-known susceptibility of millimeter wave links to human blockage and client mobility has recently motivated researchers to propose approaches that leverage both 802.11ad radios (operating in the 60 GHz band) and legacy 802.11ac radios (operating in the 5 GHz band) in dual-band commercial off-the-shelf devices to simultaneously provide Gbps throughput and reliability. One such approach is via Multipath TCP (MPTCP), a transport layer protocol that is transparent to applications and requires no changes to the underlying wireless drivers. However, MPTCP (as well as other bundling approaches) have only been evaluated to date in 60 GHz WLANs with laptop clients. In this work, we port for first time the MPTCP source code to a dual-band smartphone equipped with an 802.11ad and an 802.11ac radio. We discuss the challenges we face and the system-level optimizations required to enable the phone to support Gbps data rates and yield optimal MPTCP throughput (i.e., the sum of the individual throughputs of the two radios) under ideal conditions. We also evaluate for first time the power consumption of MPTCP in a dual-band 802.11ad/ac smartphone and provide recommendations towards the design of an energy-aware MPTCP scheduler. We make our source code publicly available to enable other researchers to experiment with MPTCP in smartphones equipped with millimeter wave radios.