The cellular network has undergone rapid progress since its inception in 1980s. While rapid iteration of newer generations of cellular technology plays a key role in this evolution, the incremental and eventually wide deployment of every new technology generation also plays a vital role in delivering the promised performance improvement. In this work, we conduct the first metamorphosis study of a cellular network generation, 5G, by measuring the user-experienced 5G performance from 5G network's birth (initial deployment) to maturity (steady state). By analyzing a 4-year 5G performance trace of 2.65M+ Ookla® ~Speedtest Intelligence® ~measurements collected in 9 cities in the United States and Europe from January 2020 to December 2023, we unveil the detailed evolution of 5G coverage, throughput, and latency at the quarterly granularity, compare the performance diversity across the 9 representative cities, and gain insights into compounding factors that affect user-experienced 5G performance, such as adoption of 5G devices and the load on the 5G network. Our study uncovers the typical life-cycle of a new cellular technology generation as it undergoes its ''growing pain'' towards delivering its promised QoE over the previous technology generation.

In 2022, 3 years after the initial 5G rollout, through a cross-country US driving trip (from Los Angeles to Boston), the authors of [28] conducted an in-depth measurement study of user-perceived experience (network coverage, performance, and QoE of a set of major 5G ''killer'' apps) over all three major US carriers. The study revealed disappointingly low 5G coverage and suboptimal network performance -- falling short of the expectations needed to support the new generation of 5G ''killer apps. Now, five years into the 5G era, widely considered its midlife, 5G networks are expected to deliver stable and mature performance. In this work, we replicate the 2022 study along the same coast-to-coast route, evaluating the current state of cellular coverage and network and application performance across all three major US operators. While we observe a substantial increase in 5G coverage and a corresponding boost in network performance, two out of three operators still exhibit less than 50% 5G coverage along the driving route even five years after the initial 5G rollout. We expand the scope of the previous work by analyzing key lower-layer KPIs that directly influence the network performance. Finally, we introduce a head-to-head comparison with Starlink's LEO satellite network to assess whether emerging non-terrestrial networks (NTNs) can complement the terrestrial cellular infrastructure in the next generation of wireless connectivity.

Despite 5G’s promise of enhanced capacity and lower latency, recent measurement studies have shown that users experience significant cellular performance degradation under vehicular mobility. In this paper, we go beyond prior work that merely characterizes 5G performance, by uncovering the underlying mechanisms responsible for throughput degradation episodes during vehicular mobility. Through extensive measurements across 3,687 km of driving routes in the U.S., we collect granular performance and network KPI data from three major carriers across diverse radio access technologies. We introduce a novel KPI-driven clustering methodology that not only quantifies the frequency and duration of performance degradations, but also critically identifies their root causes by analyzing KPIs and their interactions. Our analysis reveals previously unidentified patterns: persistent higher degradation rates in uplink versus downlink, technology-specific vulnerability signatures in LTE and 5G deployments, and the predominance of compound degradation mechanisms where multiple factors interact to create severe throughput reductions. These findings provide essential information for developing mobility-aware resource management strategies to address the unique challenges of vehicular connectivity.

We demonstrate a novel agentic control architecture for dynamic edge network reconfiguration, featuring a dual—Model Context Protocol (MCP) deployment: one instance at the gNodeB and another at the User Equipment (UE), coordinated via a shared client interface. A large language model (LLM) receives high-level user intent (e.g., "improve video call stability") and constructs a semantic action plan conditioned on real-time context and validated operational constraints. The system enables collaborative reasoning across the UE and RAN components, with human-in-the-loop authorization governing execution. Unlike conventional APIs or policy engines, MCP supports reversibility, semantic validation, and learning from feedback. This demonstration highlights the potential for intent-driven, context-aware, verifiable autonomy in edge wireless systems.

Accurate localization in dense urban areas remains a significant challenge due to the limitations of Global Navigation Satellite Systems (GNSS) in environments with obstacles and reflections, such as urban canyons. While the most recent 3GPP standards offer sophisticated network-centric positioning techniques, their widespread deployment will take time and is hindered by high infrastructure costs and complexity. In this work, we present mm-NOLOC, a UE-centric localization system, designed as a practical fallback when GNSS fails to deliver high accuracy, that leverages the growing deployment of 5G mmWave infrastructure in dense urban areas. Unlike traditional approaches, mm-NOLOC operates independently of 3GPP location support and utilizes only standardized control-plane information collected solely on the UE side – Synchronization Signal Block (SSB) Indices that are mapped to 5G mmWave beam directions – to obtain robust position estimations. To address the uncertainty introduced by urban multipath, mm-NOLOC models the SSB-to-angle relationship as a discrete and multimodal distribution, based on empirical measurements in operational 5G mmWave networks, and uses a particle filter to refine position estimates by integrating probabilistic observations with UE-side motion dynamics. We validate mm-NOLOC through experiments over commercial 5G mmWave deployments, as well as trace-based simulations. Our results show that mm-NOLOC achieves a median localization error below 3 m and a 95th percentile error below 10 m, offering a practical fallback localization solution in urban canyon scenarios for 5G networks without network location support.

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 (1 year)? (2) Does 5G provide better performance than its predecessor Long-Term Evolution (LTE)? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting two year-long measurement campaigns of 5G uplink performance leveraging a custom Android app: one crowd-sourced, cross-sectional campaign spanning 8 major cities in 7 countries and two different continents (Europe and North America), and one controlled campaign focusing on mmWave deployment at a fixed location in the downtown area of Boston, MA. Our datasets 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.

As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments. This requires hardware acceleration to scale the Physical (PHY) layer performance, programmable elements in the Radio Access Network (RAN) and intelligent controllers at the edge, careful planning of the Radio Frequency (RF) environment, as well as end-to-end integration and testing. In this paper, we describe how we developed the programmable X5G testbed, addressing these challenges through the deployment of the first 8-node network based on the integration of NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA), OpenAirInterface (OAI), and a near-real-time RAN Intelligent Controller (RIC). The Aerial Software Development Kit (SDK) provides the PHY layer, accelerated on Graphics Processing Unit (GPU), with the higher layers from the OAI open-source project interfaced with the PHY through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). An E2 agent provides connectivity to the O-RAN Software Community (OSC) nearreal-time RIC. We discuss software integration, network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, as well as up to 25 emulated User Equipments (UEs), measuring a cell rate higher than 1.65 Gbps in downlink and 143 Mbps in uplink.

Five years after the initial 5G rollout, several research works have analyzed the performance of operational 5G mmWave networks. However, these measurement studies primarily focus on single-user performance, leaving the sharing and resource allocation policies largely unexplored. In this paper, we fill this gap by conducting the first systematic study, to our best knowledge, of resource allocation policies of current 5G mmWave mobile network deployments through an extensive measurement campaign across four major US cities and two major mobile operators. Our study reveals that resource allocation among multiple flows is strictly governed by the cellular operators and flows are not allowed to compete with each other in a shared queue. Operators employ simple threshold-based policies and often over-allocate resources to new flows with low traffic demands or reserve some capacity for future usage. Interestingly, these policies vary not only among operators but also for a single operator in different cities. We also discuss a number of anomalous behaviors we observe in our experiments across different cities and operators.

With the evolution of stereoscopic video technology, live video streaming stands at the threshold of a more engaging and immersive future. Apple's recent innovations in video and Extended Reality (XR) technologies have paved the way for immersive live video experiences. This work characterizes Apple's spatial video and explores its potential in live video streaming, providing insights into the future of video streaming applications.

Ubiquitous connectivity is vital for emerging applications like extended reality, factory automation, and robotics, necessitating low latency, high data rates, and reliability in both downlink and uplink. From the network protocol perspective, successfully supporting these new use cases hinges on the network being resilient enough to address the heterogeneous demand in dynamic channel conditions. To assess the performance of legacy 5G networks for these applications, we focus on the physical (PHY) layer and analyze the existing 5G time division duplexing (TDD) method in terms of throughput. Our preliminary experiments with 3rd Generation Partnership Protocol (3GPP) compliant Matlab 5G toolbox reveal limitations of the fixed configuration of the PHY frames, that are typically used by commercial 5G networks, hindering adaptability to heterogeneous demands and compromising quality of service (QoS). To overcome this, we propose a machine learning-enabled optimization framework facilitating proactive PHY frame reconfiguration based on realtime prediction of wireless channel metrics computed at User Equipment (UE). Implementation and validation of our approach on the 3GPP-compliant Open Air Interface (OAI) 5G testbed demonstrate the practicality of our solution and its adherence to 3GPP standards. Overall, our dynamic PHY frame configuration approach consistently meets overall traffic demands better than any fixed configuration across various scenarios, while also having the lowest percentage of un-transmitted bytes in each scenario.

Multiple-Input Multiple-Output (MIMO) systems have been presented for Terahertz (THz) communications to combat high path loss and enable Terabit-per-second (Tbps) links. The lack of experimental MIMO channel measurements, however, has restricted the majority of THz MIMO work to the theoretical realm. This paper presents a novel, first-of-its-kind correlation-based MIMO channel sounder with a timing reference for sub-THz communications, which enables broadband, long-range, and time-varying channel characterization of MIMO systems. Preliminary results from a conference room setting show that 28.5% of the tested scenarios can support spatial multiplexing, and even with beamforming, the capacity outperforms the SISO cases substantially.

After 4 years of rapid deployment in the US, 5G is expected to have significantly improved the performance and overall user experience of mobile networks. However, recent measurement studies have focused either on static performance or a single aspect (e.g., handovers) under driving conditions of 5G, and do not provide a complete picture of cellular network performance today under driving conditions - a major use case of mobile networks. Through a cross-continental US driving trip (from LA to Boston, 5700km+), we conduct an in-depth measurement study of user-perceived experience (network coverage/performance and QoE of a set of major latency-critical 5G "killer'' apps) To understand the root cause of the observed network performance, while collecting low-level 5G statistics and signaling messages. Our study shows disappointingly low coverage of 5G networks today under driving and highly fragmented coverage by cellular technologies. More importantly, network and application performance are often poor under driving even in areas with full 5G coverage. We also examine the correlation of technology-wise coverage and performance with geo-location and the vehicle's speed and analyze the impact of a number of lower layer KPIs on network performance.

For its stringent QoE requirement, augmented reality (AR) has been widely hailed as a representative of ultra-high bandwidth and ultra-low latency apps that will be enabled by 5G networks/edge clouds. Such a portrait of AR by the telco and cloud industry raises an important research question - can 5G enable latency-critical applications such as (edge-assisted) AR? In this paper, we conduct to our knowledge the first in-depth measurement study of whether 5G mmWave in combination with in-network edge cloud can support the baseline edge-assisted object detection. After we discover 5G mmWave is unlikely to achieve the level of uplink network performance needed to support a baseline edge-assisted object detection implementation in the near future, we quantify the performance benefits in retrofitting app-level optimizations developed in the pre-5G era on top of baseline edge-assisted object detection, as well as the performance benefits from hardware upgrade on the edge. We find that these optimizations can significantly boost object detection performance over both LTE and 5G mmWave; however, the improvement with 5G mmWave over LTE is marginal, and 5G mmWave still fails to provide satisfactory performance in all scenarios under consideration. Overall, we conclude that today's 5G mmWave deployment is not a deciding factor in enabling edge-assisted object detection.

Despite the surge in commercially available multiplayer mixed reality (XR) devices and applications, few studies have focused on the player experience during co-located, multiplayer gameplay in XR environments. To address this gap, we designed and developed Escape from Kyle-Earth, a co-located, multiplayer XR game that can be played in both AR and VR using a head-mounted display. We then conducted a user study with 26 participants, in which each participant played both versions of the game. Our results indicate that VR evoked a stronger sense of presence while its AR counterpart increased co-presence between players. However, there was no significant difference in game enjoyment between the two platforms. Our work contributes to the burgeoning literature on co-located immersive gaming.

5G networks are being deployed rapidly across the world and have opened door to many uplink-oriented, bandwidth-intensive applications such as Augmented Reality and Connected Autonomous Vehicles. However, the roll-out is still in the early phase and the nature of deployment also varies across different geographic regions of the world. In this demo, we present NextG-UP, an Android-based tool designed to help understand the performance and evolution of 5G networks around the world. The crowd-sourcing mobile app collects various cellular network metrics and runs a short uplink throughput/latency test.

5G networks are being deployed rapidly across the world and have opened door to many uplink-oriented, bandwidth-intensive applications such as Augmented Reality and Connected Autonomous Vehicles (CAV). However, the roll-out is still in the early phase and the nature of deployment also varies across different geographic regions of the world. In this demo, we present NextG-UP, an open-source Android-based tool designed to help understand the performance and evolution of 5G networks around the world. The crowd-sourcing mobile app collects various cellular network metrics and runs a short uplink throughput/latency test.

5G mmWave is being rapidly deployed by all major mobile operators. With the technology still in its infancy, several early research works analyze the performance of operational 5G mmWave networks. Nonetheless, these measurement studies primarily focus on single-user performance, leaving the sharing and resource allocation policies largely unexplored. In this paper, we fill this gap by conducting the, to our best knowledge, first systematic study of resource allocation policies of current 5G mmWave mobile network deployments through an extensive measurement campaign across four major US cities and two major mobile operators. Our study reveals that resource allocation among multiple flows is strictly governed by the cellular operators and flows are not allowed to compete with each other in a shared queue. Operators employ simple threshold-based policies and often over-allocate resources to new flows with low traffic demands or reserve some capacity for future usage. Interestingly, these policies vary not only among operators but also for a single operator in different cities. We also discuss a number of anomalous behaviors we observed in our experiments across different cities and operators.

Future WLAN devices will combine both IEEE 802.11ad and 802.11ac interfaces. The former provides multi-Gbps rates but is susceptible to blockage, whereas the latter is slower but offers reliable connectivity. A fundamental challenge is thus how to combine those complementary technologies, to make the most of the advantages they offer. In this work, we leverage Multipath TCP (MPTCP) to use both interfaces simultaneously in order to achieve a higher overall throughput as well as seamlessly switch to a single interface when the other one fails. We find that standard MPTCP often performs sub-optimally and can yield a throughput much lower than that of single path TCP over the faster of the two interfaces. We analyze the cause of these performance issues in detail and then designMuSher, an agile MPTCP scheduler that allows MPTCP to fully utilize the channel resources available to both interfaces. Our evaluation in realistic scenarios shows thatMuShercan provide a throughput improvement of 50%/130% under WLAN/Internet settings respectively, compared to the default MPTCP scheduler. It further speeds up the recovery of a traffic stream after disruption by a factor of 8x/75x.

We present an extensive experimental evaluation of the performance and power consumption of the 60 GHz IEEE 802.11ad technology on commercial smartphones. We also compare 802.11ad against its main competitors in the 5 GHz band - 802.11ac and, for first time, 802.11ax, on mobile devices. Our performance comparison focuses on two aspects that have not been extensively studied before: (i) dense multi-client and multi-AP topologies and (ii) popular mobile applications under realistic mobility patterns. Our power consumption study covers both non-communicating and communicating modes. We also present the first study of the power saving mode in 802.11ad-enabled smartphones and its impact on performance. Our results show that 802.11ad is better able to address the needs of emerging bandwidth-intensive applications in smartphones than its 5 GHz counterparts. At the same time, we identify several key research directions towards realizing its full potential.

Wireless networks operating in the 60 GHz band have the potential to provide very high throughput but face a number of challenges (e.g., high attenuation, beam training, and coping with mobility) which are widely accepted but often not well understood in practice. Understanding these challenges, and especially their actual impact on consumer-grade hardware is fundamental to fully exploit the high physical layer rates in the 60 GHz band. To this end, we perform an extensive measurement campaign using two commercial off-the-shelf 60 GHz routers in real-world environments. Our results allow us to revisit a range of issues and provide much deeper insights into the reasons for specific performance compared to prior work on performance characterization. Further, our study goes beyond basic link characterization and explores for the first time practical considerations such as coverage and access point deployment. While some of our observations are expected, we also obtain highly surprising insights that challenge the prevailing wisdom in the community. We derive the shortcomings of current commercial 60 GHz devices, and the fundamental problems that remain open on the way to fast and efficient 60 GHz networking.

We conduct one of the first extensive experimental studies of the two main link adaptation mechanisms in 60 GHz WLANs, namely rate adaptation and beam adaptation. We first show, using a variety of commodity 60 GHz devices, that simple heuristics, used by these devices to determine which the two mechanisms should be triggered, can lead to wrong decisions even in seemingly simple scenarios. We then explore for first time the feasibility of leveraging PHY layer information and ML to guide link adaptation, using a large dataset collected with a 60 GHz software-define radio testbed in a variety of indoor environments and scenarios. Finally, we design LiBRA, a practical, standard-compliant link adaptation framework that leverages ML and PHY layer information to determine when to trigger link adaptation and which adaptation mechanism to use. LiBRA strikes a balance between throughput and link recovery delay, performing close to an oracle solution, and outperforming significantly two simple heuristics used by off-the-shelf devices.

While live 360° video streaming provides an enriched viewing experience, it is challenging to guarantee the user experience against the negative effects introduced by start-up delay, event-to-eye delay, and low frame rate. It is therefore imperative to understand how different computing tasks of a live 360° streaming system contribute to these three delay metrics. Although prior works have studied commercial live 360° video streaming systems, none of them has dug into the end-to-end pipeline and explored how the task-level time consumption affects the user experience. In this paper, we conduct the first in-depth measurement study of task-level time consumption for five system components in live 360° video streaming. We first identify the subtle relationship between the time consumption breakdown across the system pipeline and the three delay metrics. We then build a prototype Zeus to measure this relationship. Our findings indicate the importance of CPU-GPU transfer at the camera and the server initialization as well as the negligible effect of 360° video stitching on the delay metrics. We finally validate that our results are representative of real world systems by comparing them with those obtained with a commercial system.

In this paper, we conduct an extensive experimental study of the two primary link adaptation mechanisms in 60 GHz WLANs, namely rate adaptation and beam adaptation, using a large data set collected from a 60 GHz software-defined radio testbed. First, we compare the effectiveness of the two mechanisms in a variety of indoor environments and scenarios, including linear and angular displacement, mobility, blockage, and interference. Next, we study the effectiveness of two rate adaptation approaches -- SNR-based rate adaptation, which has been proposed by recent works, and a learning-based approach using PHY layer information. Our results show that the former performs poorly in practical scenarios, while the latter is promising, especially when combined with online training. Finally, we explore the effectiveness of maintaining backup beams to speedup link recovery and reduce the beam training overhead. We show that this heuristic fails in scenarios involving angular displacement on the receiver side but is quite effective in most other scenarios.

With millimeter wave (mmWave) wireless communication envisioned to be the key enabler of next generation high data rate wireless networks, security is of paramount importance. While conventional security measures in wireless networks operate at a higher layer of the protocol stack, physical layer security utilizes unique device dependent hardware features to identify and authenticate legitimate devices. In this work, we identify that the manufacturing tolerances in the antenna arrays used in mmWave devices contribute to a beam pattern that is unique to each device, and to that end we propose a novel device fingerprinting scheme based on the unique beam pattern of different codebooks used by the mmWave devices. Specifically, we propose a fingerprinting scheme with multiple access points (APs) to take advantage of the rich spatial-temporal information of the beam pattern. We perform comprehensive experiments with commercial off-the-shelf mmWave devices to validate the reliability performance of our proposed method under various scenarios. We also compare our beam pattern feature with a conventional physical layer feature namely power spectral density feature (PSD). To that end, we implement PSD feature based fingerprinting for mmWave devices. We show that the proposed multiple APs scheme is able to achieve over 99% identification accuracy for stationary LOS and NLOS scenarios and significantly outperform the PSD feature fingerprinting method. For mobility scenario, the overall identification accuracy is 99%. In addition, we perform security analysis of our proposed beam pattern fingerprinting system and PSD fingerprinting system by studying the feasibility of performing impersonation attacks. We design and implement an impersonation attack mechanism for mmWave wireless networks using state-of-the-art 60 GHz software defined radios. We discuss our findings and their implications on the security of the mmWave wireless networks.

Indoor localization has been an active research area given the popularity of Location-Based Services. The CSI fingerprinting based approach is one of the most practical and effective approaches since it can provide adequate accuracy with low overhead for users. The key drawback that limits its wide application is the huge amount of human effort required to build the fingerprint map. This paper is the first to explore addressing this limitation by automating CSI map construction using an Unmanned Aerial Vehicle (UAV). Given the limited battery capacity of commodity UAVs, it is extremely important yet challenging to optimize energy efficiency for the UAV during the CSI measurement task. To address this challenge, we formulate an energy optimization problem based on a novel graph model that includes the cost of possible actions for UAVs. We then transform the formulated problem to the classic Generalized Traveling Salesman Problem (GTSP), which can be solved efficiently. We implement the system on an off-the-shelf programmable drone equipped with a CSI measurement module. We achieve great energy efficiency improvement over the conventional coverage path planning algorithm. Meanwhile, accurate indoor localization can be achieved using the CSI data collected by our UAV system.

With the advent of bandwidth-hungry applications and the new advancements in wireless LAN standards (802.11ad, 802.11ay) and cellular technologies (5G), modern smartphones need to be able support multi-Gbps data rates. In this work, we explore if today's smartphones are capable of handling such high-speed network traffic. Using two high-end smartphones, we show that, contrary to previous beliefs, they can indeed support Gbps data rates without significant strain on their hardware resources. Using projections, we further show that up to 12.8 Gbps could be supported with just 50% CPU utilization. Finally, we explore the factors that make this possible and the contribution of each of them.

Future WLAN devices will combine both IEEE 802.11ad and 802.11ac interfaces. The former provides multi-Gbps rates but is susceptible to blockage, whereas the latter is slower but offers reliable connectivity. A fundamental challenge is thus how to combine those complementary technologies, to make the most of the advantages they offer. In this work, we explore leveraging Multipath TCP (MPTCP) to use both interfaces simultaneously in order to achieve a higher overall throughput as well as seamlessly switching to a single interface when the other one fails. We find that standard MPTCP often performs sub-optimally and may even yield a throughput much lower than that of single path TCP over the faster of the two interfaces. We analyze the cause of these performance issues in detail and then design MuSher, an agile MPTCP scheduler that allows MPTCP to fully utilize the channel resources available to both interfaces. Our evaluation in realistic scenarios shows that MuSher provides a throughput improvement of up to 1.5x/2.3x and speeds up the recovery of a traffic stream, after disruption, by a factor of up to 8x/75x, under WLAN/Internet settings respectively, compared to the default MPTCP scheduler.

LTE in unlicensed 5 GHz bands is being deployed by mobile operators for increased capacity. In this paper, we conduct an extensive measurement study with commodity LTE and Wi-Fi hardware to identify key coexistence challenges. Our study -- the first to include a commercial LAA base station -- confirms that LTE interference causes WiFi performance to degrade, harming 802.11ac high-throughput features. We then present DeMiLTE -- a system for commodity enterprise WiFi APs that detects, quantifies, and reacts to LTE interference. To our best knowledge, our solution is the first that achieves fair coexistence without modifying the LTE PHY/MAC, while still being fully-compliant to the 802.11ac standard. DeMiLTE's architecture is based on lightweight per-link interference detection and enables WiFi APs to mitigate LTE-induced performance degradation with minimal overhead. Our evaluation results show that DeMiLTE can provide up to 110% throughput gains and alleviate client disruption caused by LTE interference.

Due to the increasing density of wireless devices, the ever-growing demands for extremely high data rates, and the spectrum scarcity at the sub-6 GHz bands, making use of the spectrum-rich millimeter-wave (mmWave) frequencies is among the most important technology trends for future wireless networks. The major commercial potential of mmWave networks has led to mmWave being considered a key element for 5G-and-beyond mobile cellular networks, as well as for emerging Gbps-speed Wi-Fi networks based on the IEEE 802.11ad and draft IEEE 802.11ay standards. Despite this intense interest in mmWave communications from both the research community and industry, much fundamental research is still needed, especially at the higher layers of the networking stack.

Dual-channel screen-camera communication has been proposed to enable simultaneous screen viewing and hidden screen-camera communication. However, it strictly requires a well-controlled camera-screen alignment and an obstacle-free access. In this paper, we propose Dolphin, a novel real-time acoustics-based dual-channel communication system. Leveraging masking effects of human auditory system and readily available audio signals, Dolphin enables real-time unobtrusive speaker-microphone data communication without affecting the primary audio-hearing experience of human users. Compared with screen-camera communication, Dolphin supports non-line-of-sight transmissions and more flexible speaker-microphone alignments. Dolphin can also automatically adapt the data rate to various channel conditions. We further develop a secure data broadcasting scheme on Dolphin, where only designated privileged users can recover the embedded information in the acoustic signals. Our Dolphin prototype, built using COTS (Commercial Off-The-Shelf) smartphones, realizes (potentially secure) real-time hidden information communication, supports up to 8-meter signal capture distance and +900 listening angle, and achieves an average goodput of 240 bps at 2 m.

The advent of LTE into the unlicensed spectrum has necessitated the understanding of its operational efficiency when sharing spectrum with different radio access technologies. Our study reveals that LTE, owing to its inherent transmission characteristics, suffers significant performance degradation in the presence of interference caused by hidden terminals. This motivates the need for interference-awareness in LTE's channel access in unlicensed spectrum. To address this problem, we propose ELI. ELI's three-pronged solution equips the LTE base station with novel techniques to: (a) accurately detect and measure interference caused by hidden terminals, (b) collect interference statistics from clients across different channels with affordable overhead, and (c) leverage interference-awareness to improve its channel access performance. Our evaluations show that ELI can achieve 1.5-2x throughput gains over baseline schemes. Finally, ELI is LTE-LAA/MulteFire-standard compliant and can be deployed over the existing LTE-LAA implementation without any modifications.

The millimeter-wave (mmWave) frequency band has been utilized in the IEEE 802.11ad standard to achieve multi-Gbps throughput. Despite the advantages, mmWave links are highly vulnerable to both user and environmental mobility. Since mmWave radios use highly directional antennas, the line-of-sight (LOS) signal can be easily blocked by various obstacles, such as walls, furniture, and humans. In the complicated indoor environment, it is highly possible that the blocked mmWave link cannot be restored no matter how the access point and the mobile user change their antenna directions. To address the problem and enable indoor mobile mmWave networks, in this paper, we introduce the reconfigurable 60 GHz reflect-arrays to establish robust mmWave connections for indoor networks even when the links are blocked by obstructions. First, the reconfigurable 60 GHz reflect-array is designed, implemented, and modeled. Then a three-party beam-searching protocol is designed for reflect-array-assisted 802.11ad networks. Finally, an optimal array deployment strategy is developed to minimize the link outage probability in indoor mobile mmWave networks. The proposed solution is validated and evaluated by both in-lab experiments and computer simulations.

Multi-stream 60 GHz communication has the potential to achieve data rates up to $100$ Gbps via multiplexing multiple data streams. Unfortunately, establishing multi-stream directional links can be a high overhead procedure as the search space increases with the number of spatial streams and the product of AP-client beam resolution. In this paper, we present MUlti-stream beam-Training for mm-wavE networks (MUTE) a novel system that leverages channel sparsity, GHz-scale sampling rate, and the knowledge of mm-Wave RF codebook beam patterns to construct a set of candidate beams for multi-stream beam steering. In 60 GHz WLANs, the AP establishes and maintains a directional link with every client through periodic beam training. MUTE repurposes these beam acquisition sweeps to estimate the Power Delay Profile (PDP) of each beam with zero additional overhead. Coupling PDP estimates with beam pattern knowledge, MUTE selects a set of candidate beams that capture diverse or ideally orthogonal paths to obtain maximum stream separability. Our experiments demonstrate that MUTE achieves 90% of the maximum achievable aggregate rate while incurring only 0.04% of exhaustive search's training overhead.

We present LiSteer, a novel system that steers mmWave beams at mobile devices by repurposing indicator LEDs on wireless Access Points (APs) to passively acquire direction estimates using off-the-shelf light sensors. We demonstrate that LiSteer maintains beam alignment at the narrowest beamwidth level even in case of device mobility, without incurring any training overhead at mobile devices. Our extensive evaluation on a custom dual-band hardware platform comprising highly directional horn antennas as well as practical phased antenna arrays with electronic beam steering shows that LiSteer achieves direction estimates within 2.5 degrees of ground truth on average. Moreover, it achieves beam steering accuracy of more than 97% while in tracking mode, without incurring any client beam training or feedback overhead.

Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.

802.11ad links provide data rates up to 6.7 Gbps but remain highly susceptible to blockage and mobility. On the other hand, legacy 802.11ac/n links yield much lower rates but are robust even under dynamic scenarios. In this work, we explore using Multipath TCP (MPTCP) to engage both 802.11ad and 802.11ac interfaces simultaneously for performance speed-up and improved reliability. We show that vanilla MPTCP achieves these goals under static conditions but often results in performance worse than using the faster interface alone under dynamic scenarios. We then design and implement AMuSe, a new MPTCP scheduler that allows MPTCP to perform near-optimally under all scenarios.

Wireless networks operating in the 60 GHz band have the potential to provide very high throughput but face a number of challenges (e.g., high attenuation, beam training, and coping with mobility) which are widely accepted but often not well understood in practice. Understanding these challenges, and especially their actual impact on consumer-grade hardware is fundamental to fully exploit the high physical layer rates in the 60 GHz band. To this end, we perform an extensive measurement campaign using two commercial off-the-shelf 60 GHz routers in practical real-world environments. Our study is centered around two fundamental adaptation mechanisms in 60 GHz networks-beam training and rate control- whose interactions are key for performance. Understanding these interactions allows us to revisit a range of issues and provide much deeper insights into the reasons for specific performance compared to prior work on performance characterization. Further, our study goes beyond basic link characterization and explores for the first time practical considerations such as coverage and access point deployment. While some of our observations are expected, we also obtain highly surprising insights that challenge the prevailing wisdom in the community.

The use of directional antennas in millimeter-wave communication promises high spatial reuse at multi-gigabit-per-second data rates in dense wireless networks. Existing work studies such networks using commercial hardware but is limited to individual links. Moreover, such hardware typically allows for little or no control of the lower layers of the protocol stack. In this paper, We study the performance of dense millimeterwave deployments featuring up to eight stations. To this end, we use a practical IEEE 802.11ad millimeter-wave testbed that allows access to the lower layer parameters of each station. This enables us to analyze the impact of these parameters on upper layer performance. We study, for first time to our best knowledge, issues such as the impact of channel contention on the buffer size at the transport layer, the effect of frame aggregation, and the efficiency of spatial sharing. Our results show that using large buffer sizes with TCP is harmful due to channel contention despite the multi-gigabit-per-second data rates. Further, frame aggregation is only beneficial up to a certain level due to higher error rates for large frames. Finally, we also study delay, showing that the regular beacon transmission time can degrade performance.

Deploying LTE networks in unlicensed spectrum requires us to move beyond coexistence mechanisms and understand the suitability of LTE's synchronous operation in a spectrum that is governed by asynchronous access principles. Our study reveals a fundamental conflict in LTE uplink access that arises between the scheduled nature of LTE's multi-user transmissions -- critical for leveraging the diversity (OFDMA) and multiplexing (multi-user MIMO) gains -- and the asynchronous nature of interference on the clients. The result is a significant loss in spectrum utilization and throughput that scales with the number of interfering terminals.

The knowledge of link packet loss rates (PLRs) at different PHY layer configurations is vital for a number of wireless network optimization schemes. However, the very large number of PHY layer configurations offered by modern 802.11 n/ac networks has made probing-based PLR estimation at each available configuration extremely challenging. In this paper, we seek to answer the question “How to estimate the PLRs at each available PHY layer configuration with minimal overhead?” Our analysis of the PLR datasets collected from three 802.11 n/ac testbeds reveals that, for any given link, there are several configurations with similar PLR. However, capturing this similarity using well-known link quality indicators like RSSI, or PHY layer features such as MCS or number of MIMO streams is hard. Consequently, we explore the approach of clustering the available PHY layer configurations into a small number of clusters with similar PLR, independent of any other parameter, and only probe one representative configuration in each cluster. Using two real-world case studies - rate adaptation and multihop routing, we show that the proposed clustering-based PLR estimation helps network optimization schemes to reach optimal configurations faster leading to significant performance improvements.

We introduce X60, the first SDR-based testbed for 60 GHz WLANs, featuring fully programmable MAC/PHY/Network layers, multi-Gbps rates, and a user-configurable 12-element phased antenna array. These features provide us with an unprecedented opportunity to revisit the most important aspects of 60 GHz signal propagation and obtain new insights on performance expected from practical 60 GHz systems. X60's unique capabilities make it an ideal platform for experimentation and prototyping across layers.

This work conducts one of the first experimental studies of Multipath TCP (MPTCP) in dual-band 60 GHz/5 GHz WLANs using off-the-shelf hardware. We consider both uncoupled and different coupled congestion control algorithms, compare their performance and their potential to improve throughput over single path TCP, and uncover their limitations. In contrast to a recent study that reports reduced throughput with MPTCP compared to single path TCP over 60 GHz, our results show that significant performance improvements are possible, especially in the case of uncoupled congestion control. On the other hand, performance gains with coupled congestion control are lower as these algorithms often fail to fully utilize the capacity of both paths simultaneously. We also observe a pathological case that can lead to significantly reduced throughput with MPTCP regardless of the congestion control algorithm.

Location information has become a key component of many applications in mobile and pervasive computing, and the ability to accurately predict the mobility of clients allows these applications to provide better service. However, existing location predictors rely heavily on a significant amount of empirical knowledge to function well. In this paper, we develop a novel framework to predict unknown locations when only little location information is available. Specifically, we first extract WiFi locations from WiFi scan results, then a mobility model is built based on the resulted WiFi location graph with connectivity information, finally, we make location predictions with little known location data with Gibbs sampling over the mobility model. Using a data set containing 31 fairly complete WiFi traces collected over three months as ground truth, we compare our proposed approach with other existing state-of-the-art location predictors. The experimental results show that our framework can achieve 83% location prediction accuracy with only three location samples each day, 15% better than Markov and Bayesian predictors which heavily rely on empirical knowledge.

The millimeter-wave technology is emerging as an alternative to legacy 2.4/5 GHz WiFi, offering multi-Gigabit throughput. While a lot of attention has been paid recently to analyzing the performance of the 60 GHz technology and adapting it for indoor WLAN usage, the power consumption aspect has largely been neglected. Given that mobile devices are the next target for 60 GHz, any discussion about this technology is incomplete without considering power consumption. In this work, we present the first, to our best knowledge, detailed study of the power consumption of 60 GHz commodity devices. We evaluate the power and energy consumption of two standard-compliant 60 GHz wireless adapters in different operating states and under a number of different configurations. We also compare our results against 802.11ac and discuss power-performance tradeoffs for the two technologies.

We conduct an extensive experimental evaluation of a class of WiFi active power/energy consumption models for smartphones that are based on parameters readily available to the upper layers of the protocol stack. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. We study the properties of a previously proposed throughput-based model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios that have not been considered in previous studies. Our results show that the model works well in a number of scenarios, with both 802.11nand 802.11ac-equipped smartphones, and its accuracy can be largely improved with the knowledge of transport layer protocol and packet size. However, such knowledge makes the model more complex and results in largely reduced accuracy in high throughput settings or on hardware different from the one that was used for training. We further discuss a few practical issues related to the measurement and modeling methodology.

The static capacity provisioning in traditional WiFi networks (WLANs) cannot cope with the high spatiotemporal traffic variations in high-density venues such as conference centers, stadiums etc. To guarantee reliable performance, venue owners are forced to over-provision their WLANs based on worst-case traffic demand estimations, increasing capital and operational expenses. We propose AmorFi, a radically new way of deploying WLANs to handle peak traffic demands with average-case provisioning. Our key idea is to decouple baseband processing from RF transmission (inspired by the cloud-RAN concept in cellular networks) and introduce software programmability to flexibly allocate WiFi capacity in real time based on varying traffic demands. We implement AmorFi using off-the-shelf WiFi APs over a RF-over-fiber cloud-RAN testbed. Our experiments and simulations demonstrate that the software-defined capacity allocation enabled with AmorFi delivers more than $2x$ throughput than traditional WLANs.

This work explores the idea of building multi-Gigabit indoor enterprise WLANs using the millimeter-wave technology. Instead of the legacy 2.4/5 GHz band, we look at the unlicensed band around 60 GHz and the new 802.11ad standard, which supports PHY data rates of up to 6.76 Gbps. Through a set of measurements with commercial hardware, we show the feasibility of building multi-Gigabit WLANs. Using observations from our experiments and results reported in recent studies, we point out both the advantages and drawbacks of the 60 GHz technology, and identify the research challenges towards making multi-Gigabit 60 GHz indoor WLANs a reality.

A large number of routing protocols for Cognitive radio networks (CRNs) have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. In our previous work, we conducted the first extensive empirical performance study of routing protocols for CRNs-Coolest Path, SAMER, and CRP - under the same realistic set of assumptions. Our study revealed that SAMER significantly outperforms the other protocols under low primary user activity but its performance degrades under high PU activity. In this work, we introduce an enhanced version of SAMER (SAMER+) that performs consistently well in both low and high PU activity scenarios. The core of SAMER+ is a new path metric that balances short term throughput and long term path stability. We evaluate SAMER+ using extensive simulations and compare it against SAMER and Coolest Path. Our results show that SAMER+ significantly outperforms both protocols in a variety of scenarios.

This paper presents a feasibility study of 60 GHz indoor WLANs. We evaluate 60 GHz performance in a typical academic office building under the primary assumption that 60 GHz WLAN APs and clients will be equipped with wider beam antennas to cope with client mobility. In contrast to previous works which measured performance at a single layer using custom, non-standard compliant hardware, we investigate performance across multiple layers using 802.11ad-compliant wide beam COTS devices. Our study shows that the large number of reflective surfaces in typical indoor WLAN environments combined with wider beams makes performance highly unpredictable and invalidates several assumptions that hold true in static, LOS scenarios. Additionally, we present the first measurements, to our best knowledge, of power consumption of an 802.11ad NIC and examine the impact of a number of factors on power consumption.

During the one minute it takes to read this abstract, two billion smartphones worldwide will perform billions of Wifi channel scans recording the signal strength of nearby Wifi Access Points (APs). Yet despite this ongoing planetary-scale wireless network measurement, few systematic efforts are made today to recover this potentially valuable data. In this paper we ask the question: “Are the smartphone channel scans useful in monitoring enterprise Wifi networks?” More specifically, can these client-side measurements provide new insights compared to the AP-side measurements that enterprise Wifi networks already perform? Beginning with two Wifi scan datasets collected on two large scale smartphone testbeds, we conduct case studies that show how smartphone channel scans can be used to (1) improve AP spectrum management, and (2) predict the impact of AP failure or overload. In each case, a walk on the client side yields valuable insights for network operators that are otherwise impossible to gain from AP-side measurements, and together our results demonstrate the value of smartphone channel scans.

With the ever-increasing use of smart devices, recent research endeavors have led to unobtrusive screen-camera communication channel designs, which allow simultaneous screen viewing and hidden screen-camera communication. Such practices, albeit innovative and effective, require well-controlled alignment of camera and screen and obstacle-free access.

With the ever-increasing use of smart devices, recent research endeavors have led to unobtrusive screen-camera communication channel designs, which allow simultaneous screen viewing and hidden screen-camera communication. Such practices, albeit innovative and effective, require well-controlled alignment of camera and screen and obstacle-free access. In this demo, we present Dolphin, a novel form of real-time acoustics-based dual-channel communication, which uses a speaker and the microphones on off-the-shelf smartphones to achieve concurrent audible and hidden communication. By leveraging masking effects of the human auditory system and readily available audio signals in our daily lives, Dolphin ensures real-time unobtrusive speaker-microphone data communication, while, at the same time, it overcomes the main limitations of existing screen-camera links.

WiFi activity is a major source of power consumption in today's smartphones. Consequently, accurately WiFi power consumption models are extremely useful for researchers and app developers. Among a large number of models proposed recently, a model introduced by Serrano et al. was the first to add a new component - a per-frame energy toll incurred as a frame traverses the protocol stack - to the power consumption of the wireless NIC. The authors called this new component cross-factor and validated the accuracy of the model on a large number of devices, mostly 802.11g wireless routers and APs. This paper examines the validity of the model introduced by Serrano et al. on today's smartphones. We try to answer two questions: (i) Can the model accurately estimate the power consumption due to WiFi activity in today's smartphones given the complexity of modern smartphone architectures? (ii) Does the model remain valid in the case of 802.11n/ac interfaces, and if yes, can it reflect the impact of the new MAC features (e.g., MIMO, channel bonding) on the WiFi power consumption? Additionally, we study the impact of the power saving mode (PSM) which was ignored in the original model and show that ignoring PSM results in significant overestimation of the total power consumption at low frame generation rates. Accordingly, we propose a new model that works across the full range of frame generation rates and verify its accuracy for a wide range of parameters and devices.

Indoor location information plays a fundamental role in supporting various interesting location- aware indoor applications. Widely deployed WiFi networks make it feasible to perform indoor localization by first establishing a received signal strength (RSS) map covering the whole area based on a signal propagation model, then determining a location from an online RSS measurement given the RSS map. However, challenges remain in practical deployments, due to inaccurately estimated RSS values in the RSS map and insufficient number of access points (APs) in large indoor areas. To address these challenges, we develop a robust, cost-effective and scalable localization system (REAL). Our approach takes the error from the indoor radio signal propagation model into consideration. It also exploits information of unobserved APs at a given location and an optimal clustering method in the location searching phase. Our real-world experimental results demonstrate that REAL achieves considerable localization accuracy at a very low training cost even for a large indoor area. In addition, the results show that our scheme can also be effectively applied to Bluetooth networks with sparse signal coverage.

The 7 GHz wide unlicensed band centered around 60 GHz, standardized by 802.11ad, is emerging as an alternative to 2.4/5 GHz wireless systems, promising multi-Gigabit throughput. However, the Millimeter-Wave (mmWave) technology comes with its own challenges. The aim of this thesis is to overcome these challenges in order to build general-purpose 802.11ad indoor home/enterprise WLANs. Towards this goal, we perform an extensive link measurement study with COTS hardware to characterize the 60 GHz channel in an indoor setup. The results of this study will guide the design of better performing MAC algorithms and protocols. Further, we propose a relay-based WLAN architecture as a solution to the unique challenges of 60 GHz links and specify multiple research directions associated with such an architecture that are essential to the realization of multi-Gigabit indoor WLANs.

The increasing popularity of WiFi brings new challenges in the design of wireless protocols but at the same time creates exciting opportunities for novel ways of exploiting WiFi technology in a variety of applications. On one hand, the smaller form factor of today's WiFi devices allows WiFi users to access wireless networks under mobility, calling for new, mobility-aware wireless protocols that are able to sustain high performance, on the other hand, the ubiquity of WiFi devices has recently raised interest in extending WiFi's capabilities beyond communication, e.g., for human-computer interaction. In this paper, we demonstrate that it is possible to enable fine-grained human motion detection on commodity WiFi devices by exploiting PHY layer information exposed by today's WiFi chipsets. We propose two motion detection techniques for two different application scenarios and explore their benefits on protocol performance and human-device interaction, respectively.

This paper presents the first, to the best of our knowledge, detailed experimental study of 802.11n/ac throughput and power consumption in modern smartphones. We experiment with a variety of smartphones, supporting different subsets of 802.11n/ac features. We investigate the power consumption in various states of the wireless interface (sleep, idle, active), the impact of various features of 802.11n/ac (PHY bitrate, frame aggregation, channel bonding, MIMO) on both throughput and power consumption, and the tradeoffs between these two metrics. Some of our findings are significantly different from the findings of previous studies using 802.11n/ac wireless cards for laptop/desktop computers. We believe that these findings will help in understanding various performance and power consumption issues in today's smartphones and will guide the design of power optimization algorithms for the next generation of mobile devices.

This paper demonstrates that it is possible to leverage WiFi signals from commodity mobile devices to enable hands-free drawing in the air. While prior solutions require the user to hold a wireless transmitter, or require custom wireless hardware, or can only determine a pre-defined set of hand gestures, this paper introduces WiDraw, the first hand motion tracking system using commodity WiFi cards, and without any user wearables. WiDraw harnesses the Angle-of-Arrival values of incoming wireless signals at the mobile device to track the user's hand trajectory. We utilize the intuition that whenever the user's hand occludes a signal coming from a certain direction, the signal strength of the angle representing the same direction will experience a drop. Our software prototype using commodity wireless cards can track the user's hand with a median error lower than 5 cm. We use WiDraw to implement an in-air handwriting application that allows the user to draw letters, words, and sentences, and achieves a mean word recognition accuracy of 91%.

Cognitive radio networks (CRNs) have emerged as a promising solution to the ever-growing demand for additional spectrum resources and more efficient spectrum utilization. A large number of routing protocols for CRNs have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. However, little is known about the relative performance of all these protocols, let alone the tradeoffs among their different design goals. In this paper, we conduct the first detailed, empirical performance comparison of three representative routing protocols for CRNs, under the same realistic set of assumptions. Our extensive simulation study shows that the performance of routing protocols in CRNs is affected by a number of factors, in addition to PU activity, some of which have been largely ignored by the majority of previous works. We find that different protocols perform well under different scenarios, and investigate the causes of the observed performance. Furthermore, we present a generic software architecture for the experimental evaluation of CRN routing protocols on a testbed based on the USRP2 platform, and compare the performance of two protocols on a 6 node testbed. The testbed results confirm the findings of our simulation study.

We propose ANC-CDMA, a CDMA-based analog network coding scheme for underwater acoustic sensor networks (UW-ASNs) that can significantly improve the underwater acoustic channel utilization. First, we analyze a unidirectional multihop network, in which two acoustic nodes separated by two hops are assigned the same code-division channel (i.e., spreading code) to transmit concurrently. The packets transmitted by the two nodes will collide at the intermediate (relay) node. However, we show that, by exploiting a priori information, i.e., the interfered packet previously received from one of the nodes, and applying joint channel estimation through pilot supervision along with a newly designed adaptive RAKE receiver, the relay node can cancel the interference before decoding the packet of interest. We then extend our analysis to a bidirectional network, in which two nodes first concurrently transmit their packets to the relay using the same spreading code. The relay amplifies and forwards the received interfered packet to the two nodes. We show that either node can still decode the packet of interest after equalizing the channel effects introduced during the propagation from the relay to itself and jointly estimating multipath affected channels and suppressing the self-interference signal before applying the adaptive RAKE receiver. The proposed ANC-CDMA scheme is implemented in a testbed based on Teledyne Benthos Telesonar SM-975 underwater modems and tested extensively in Lake LaSalle. Experiments and simulations demonstrate that for a 1-2 dB tradeoff in signal-to-noise ratio (SNR), the proposed scheme can significantly improve the channel utilization of a unidirectional and bidirectional networks by up to 50% and 100%, respectively, compared with conventional multiuser DS-CDMA scheme.

With the proliferation of smartphones and tablets, mobile devices are soon becoming a preferred medium of Internet access in Wireless LANs (WLANs). Due to their smaller form factor, these truly mobile devices allow the users to access the wireless networks while undergoing different types of mobility, posing new challenges to wireless protocols. Current history-based protocols that maximize performance in static settings do not work well in mobile settings where wireless conditions change rapidly. Thus, today's WLANs need to be able to determine the type of the client's mobility and employ appropriate strategies in order to sustain high performance. While previous work tried to detect mobility using hints from sensors available in today's mobile devices, in this work, we demonstrate how different mobility modes can be distinguished by using physical layer information -- Channel State Information (CSI) and Time-of-Flight (ToF) -- available at commodity APs, with no modifications on the client side. In addition, we demonstrate how fine-grained mobility determination can be exploited to improve performance of client roaming, rate control, frame aggregation, and MIMO beamforming. Our testbed experiments show that our mobility classification algorithm achieves more than 92% accuracy in a variety of scenarios, and the combined throughput gain of all four mobility-aware protocols over their mobility-oblivious counterparts can be more than 100%.

The hundreds of millions of deployed smartphones provide an unprecedented opportunity to collect data to monitor, debug, and continuously adapt wireless networks to improve performance. In contrast with previous mobile devices, such as laptops, smartphones are always on but mostly idle, making them available to perform measurements that help other nearby active devices make better use of available network resources. We present the design of PocketSniffer, a system delivering wireless measurements from smartphones both to network administrators for monitoring and debugging purposes and to algorithms performing realtime network adaptation. By collecting data from smartphones, PocketSniffer supports novel adaptation algorithms designed around common deployment scenarios involving both cooperative and self-interested clients and networks. We present preliminary results from a prototype and discuss challenges to realizing this vision.

We conduct the first detailed measurement study of the properties of a class of WiFi active power/energy consumption models based on parameters readily available to smartphone app developers. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. Using a large dataset and an 802.11n-equipped smartphone, we build four versions of a previously proposed linear power-throughput model, which allow us to explore the fundamental trade off between accuracy and simplicity. We study the properties of the model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios which have not been considered in previous studies. Our study shows that the model works well in a number of scenarios but its accuracy drops with high throughput values or when tested on different hardware. We further show that a non-linear model can greatly improve the accuracy in these two cases.

The continuously increasing number of smartphones and tablets allow the users to access Wireless LANs (WLANs) while undergoing different types of mobility, posing new challenges to wireless protocols. Current history-based WLAN protocols do not work well in mobile settings where wireless conditions change rapidly. Thus, today's WLANs need to be able to determine the type of the client's mobility and employ appropriate strategies in order to sustain high performance. While previous work tried to detect mobility using hints from sensors available in mobile devices, in this work, we demonstrate how different mobility modes can be distinguished by using physical layer information - Channel State Information (CSI) and Time-of-Flight (ToF) - available at commodity APs, with no modifications on the client side. Our testbed experiments show that our mobility classification algorithm achieves more than 92% accuracy in a variety of scenarios. In addition, we demonstrate how fine-grained mobility determination can be exploited to greatly improve performance of client roaming and MIMO beamforming.

We propose a new secure underwater acoustic communication scheme designed to let a user (Alice) transmit a confidential message to another user (Bob) in the presence of an eavesdropper (Eve). A typical approach in conventional wireless physical-layer security is to rely on a friendly jammer to jam Eve through artificial noise (AN). Instead, for the first time, we propose a secure underwater communication scheme that relies on cooperative friendly jamming through CDMA-based analog network coding (ANC). The cooperative friendly jammer transmits information using the same spreading code used in the legitimate Alice-Bob link. The information transmitted by the cooperative jammer is known a priori to Bob, but not to Eve. Although the jammer's packet will also interfere at Bob, we show that after jointly estimating the two multipath-affected channels, Bob can suppress the interfering packet and decode Alice's packet, while Eve cannot. We also formulate the problem of joint optimal selection of friendly jammer and power allocation (for Alice and the jammer) that minimize Eve's capability of intercepting the signal while guaranteeing a predefined level of quality of service (QoS) for Bob. The proposed scheme is implemented in a testbed based on Teledyne Benthos Telesonar SM-975 underwater modems and tested extensively in Lake LaSalle at the University at Buffalo. Experiments and simulations demonstrate that, for a given energy budget, the proposed scheme can guarantee much higher bit error rate (BER) at Eve, while creating minimal BER disturbance at Bob, compared to the AN-aided approach.

In the recent past, link quality-based routing metrics, such as ETX and ETT, have been shown to significantly outperform the traditional hopcount metric in legacy 802.11a/b/g wireless mesh networks (WMNs). The new 802.11n standard introduces a number of enhancements at the MAC/PHY layers (MIMO technology, channel bonding, frame aggregation, and more aggressive modulation and coding schemes) marking the beginning of a new generation of 802.11 radios. In this paper, we conduct one of the first experimental studies of the performance of link quality-based routing metrics in a 21-node indoor 802.11n WMN. Our study reveals that the gains of link quality-based metrics over the hopcount metric in legacy 802.11 WMNs do not carry over in 802.11n MIMO WMNs. We analyze the causes of this behavior and make recommendations for the design of new routing metrics in 802.11n WMNs.

We conduct the first experimental study of the performance of link quality-based routing metrics in an 802.11n wireless mesh network (WMN). Link quality-based metrics have been shown to significantly outperform the traditional hopcount metric but they have only been evaluated over legacy 802.11a/b/g radios. The new 802.11n standard introduces a number of enhancements at the MAC and PHY layers (MIMO technology, channel bonding, frame aggregation, short guard interval, and more aggressive modulation and coding schemes) marking the beginning of a new generation of 802.11 radios. Our study in a 21-node indoor 802.11n WMN testbed reveals that the gains of link quality-based metrics over the hopcount metric in legacy 802.11 WMNs do not carry over in 802.11n MIMO WMNs. We analyze the causes of this behavior and make recommendations for the design of new routing metrics in 802.11n WMNs.

Cognitive radio networks (CRNs) have emerged as a promising solution to the ever-growing demand for additional spectrum resources and more efficient spectrum utilization. A large number of routing protocols for CRNs have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. However, little is known about the relative performance of all these protocols, let alone the tradeoffs among their different design goals. In this paper, we conduct the first detailed, empirical performance comparison of three representative routing protocols for CRNs, under the same realistic set of assumptions. Our extensive simulation study shows that the performance of routing protocols in CRNs is affected by a number of factors, in addition to PU activity, some of which have been largely ignored by the majority of previous works. We find that different protocols perform well under different scenarios, and investigate the causes of the observed performance. Furthermore, we present a generic software architecture for the experimental evaluation of CRN routing protocols on a test bed based on the USRP2 platform, and compare the performance of two protocols on a 6 node test bed. The test bed results confirm the findings of our simulation study.

The WiFi radio in smartphones consumes a significant portion of energy when active. To reduce the energy consumption, the Power Saving Mode was standardized in IEEE 802.11 and two major implementations, Static PSM and Dynamic PSM, have been widely used in mobile devices. Unfortunately, both PSMs have inherent drawbacks: Static PSM is energy efficient but imposes considerable extra delays on data transfers; Dynamic PSM incurs little extra delay but misses energy saving opportunities. In this paper, we first analyze a one-week trace from 10 users and show that more than 80% of all traffic are Web 2.0 flows, which are of very small sizes and short durations. Targeting these short but dominant flows, we propose a system called Percy, to achieve the best of both worlds (Static and Dynamic PSM), i.e., to maximize the energy saving while minimizing the delay of flow completion time. Percy works by deploying a web proxy at the AP and suitably configuring the PSM parameters, and is designed to work with unchanged clients running Dynamic PSM, and unchanged APs and Internet servers. We evaluate our system via trace-driven testbed experiments. Our results show that Percy saves 40-70% energy compared to Dynamic PSM configurations of Nokia, iPhone and Android, while imposing low extra delay that can hardly be perceived by users.

In contrast to unicast routing, high-throughput reliable multicast routing in wireless mesh networks (WMNs) has received little attention. There are two primary challenges to supporting high-throughput, reliable multicast in WMNs. The first is no different from unicast: Wireless links are inherently lossy due to varying channel conditions and interference. The second, known as the “crying baby” problem, is unique to multicast: The multicast source may have varying throughput to different multicast receivers, and hence trying to satisfy the reliability requirement for poorly connected receivers can potentially result in performance degradation for the rest of the receivers. In this paper, we propose Pacifier, a new high-throughput, reliable multicast protocol for WMNs. Pacifier seamlessly integrates four building blocks-namely, tree-based opportunistic routing, intraflow network coding, source rate limiting, and round-robin batching-to support high-throughput, reliable multicast routing in WMNs, while at the same time it effectively addresses the “crying baby” problem. Our experiments on a 22-node IEEE 802.11 WMN testbed show that Pacifier increases the average throughput over a state-of-the-art reliable network coding-based protocol MORE by up to 144%, while at the same time it solves the “crying baby” problem by improving the throughput of well-connected receivers by up to a factor of 14.

A primary challenge in multi casting video in a wireless LAN to multiple clients is to deal with the client diversity -- clients may have different channel characteristics and hence receive different numbers of transmissions from the AP. A promising approach to overcome this problem is to combine multi-resolution (layered) video coding with interlayer network coding. The fundamental challenge in such an approach is to determine the strategy of coding the packets across different layers that maximizes the number of decoded layers at all clients. This paper makes three contributions. (1) We first show that even for one client, the previously proposed canonical triangular scheme for inter-layer network coding can perform poorly. We show how to enhance the triangular scheme by incorporating the estimated target number of layers which significantly improves its effectiveness. (2) We show that such an enhanced triangular scheme still performs poorly for multiple clients with diverse channel characteristics, which motivates the need for searching for the optimal coding strategy. The naive way of searching for the optimal strategy is computationally prohibitive. We present several optimizations that drastically reduce the complexity of exhaustively searching for the optimal strategy, making it feasible in real time. (3) Finally, we design and evaluate an on line video delivery scheme, Percy, to be deployed at a proxy behind the AP of a wireless LAN. Our simulation results show that Percy outperforms the previous inter-layer coding heuristic by up to 22-80% with varying numbers of clients.

A primary challenge in multicasting video in a wireless LAN is to deal with the client diversity -- clients may have different channel characteristics and hence receive different numbers of transmissions from the AP. A promising approach to overcome this problem is to combine scalable video coding techniques such as MRC or MDC, which divide a video stream into multiple substreams, with inter-layer network coding. The fundamental challenge in such an approach is to determine the strategy of coding the packets across different layers that maximizes the number of decoded layers at all clients. In [7], the authors showed that inter-layer NC indeed helps the delivery of MRC coded media over the WiFi, and proposed how to efficiently search for the optimal coding strategies online.

The use of random linear network coding (NC) has significantly simplified the design of opportunistic routing (OR) protocols by removing the need of coordination among forwarding nodes for avoiding duplicate transmissions. However, NC-based OR protocols face a new challenge: How many coded packets should each forwarder transmit? To avoid the overhead of feedback exchange, most practical existing NC-based OR protocols compute offline the expected number of transmissions for each forwarder using heuristics based on periodic measurements of the average link loss rates and the ETX metric. Although attractive due to their minimal coordination overhead, these approaches may suffer significant performance degradation in dynamic wireless environments with continuously changing levels of channel gains, interference, and background traffic. In this paper, we propose CCACK, a new efficient NC-based OR protocol. CCACK exploits a novel Cumulative Coded ACKnowledgment scheme that allows nodes to acknowledge network-coded traffic to their upstream nodes in a simple way, oblivious to loss rates, and with negligible overhead. Through extensive simulations and testbed experiments, we show that CCACK greatly improves both throughput and fairness compared to MORE, a state-of-the-art NC-based OR protocol.

The use of random linear network coding (NC) has significantly simplified the design of opportunistic routing (OR) protocols by removing the need of coordination among forwarding nodes for avoiding duplicate transmissions. However, NC-based OR protocols face a new challenge: How many coded packets should each forwarder transmit? To avoid the overhead of feedback exchange, most practical existing NC-based OR protocols compute offline the expected number of transmissions for each forwarder using heuristics based on periodic measurements of the average link loss rates and the ETX metric. Although attractive due to their minimal coordination overhead, these approaches may suffer significant performance degradation in dynamic wireless environments with continuously changing levels of channel gains, interference, and background traffic. In this paper, we propose CCACK, a new efficient NC-based OR protocol. CCACK exploits a novel Cumulative Coded ACKnowledgment scheme that allows nodes to acknowledge network coded traffic to their upstream nodes in a simple way, oblivious to loss rates, and with practically zero overhead. In addition, the cumulative coded acknowledgment scheme in CCACK enables an efficient credit-based, rate control algorithm. Our evaluation shows that, compared to MORE, a state-of-the-art NC-based OR protocol, CCACK improves both throughput and fairness, by up to 20× and 124%, respectively, with average improvements of 45% and 8.8%, respectively.

In contrast to unicast routing, high-throughput reliable multicast routing in wireless mesh networks (WMNs) has received little attention. There are two primary challenges to supporting high-throughput, reliable multicast in WMNs. The first is no different from unicast: wireless links are inherently lossy due to varying channel conditions and interference. The second, known as the "crying baby" problem, is unique to multicast: the multicast source may have varying throughput to different multicast receivers, and hence trying to satisfy the reliability requirement for poorly connected receivers can potentially result in performance degradation for the rest of the receivers. In this paper, we propose Pacifier, a new high-throughput reliable multicast protocol for WMNs. Pacifier seamlessly integrates four building blocks, namely, tree-based opportunistic routing, intra-flow network coding, source rate limiting, and round-robin batching, to support high-throughput, reliable multicast routing in WMNs, while at the same time effectively addresses the "crying baby" problem. Our evaluations show that Pacifier increases the average throughput over a practical, state-of-the-art reliable network coding-based protocol MORE by 171%, while improving the throughput of well-connected receivers by up to a factor of 20.

Recently, network coding has gained much popularity and several practical routing schemes have been proposed for wireless mesh networks that exploit interflow network coding for improved throughput. However, the evaluation of these protocols either assumed simple topologies and traffic patterns such as opposite flows along a single chain, or small, dense networks which have ample overhearing of each other's transmissions in addition to many overlapping flows. In this paper, we seek to answer the fundamental question: how much performance benefit from network coding can be expected for general traffic patterns in a moderate-sized wireless mesh network? We approach this question via an empirical study of both coordinated and opportunistic coding based protocols subject to general traffic patterns. Our study shows the performance benefits under both types of coding for general traffic patterns are extremely limited. We then analyze and uncover fundamental reasons for the limited performance benefits.

There are two primary challenges to supporting high-throughput, reliable multicast in wireless mesh networks (WMNs). The first is no different from unicast: wireless links are inherently lossy due to varying channel conditions and interference. The second, known as the "crying baby" problem, is unique to multicast: the multicast source may have varying throughput to different multicast receivers, and hence trying to satisfy the reliability requirement for poorly connected receivers can potentially result in performance degradation for the rest of the receivers.

We present the design, implementation, and evaluation of a Time Division Multiplex (TDM) MAC protocol for multi-hop wireless mesh networks using a programmable wireless platform. Extensive research has been devoted to optimal scheduling algorithms for multi-hop wireless networks assuming a perfect TDM MAC protocol. However, the problem of designing and implementing such a protocol has not received due attention. We introduce a design framework that addresses the three main challenges that comprise this problem: (i) How to calibrate and optimize the TDM MAC protocol parameters given a wireless platform, (ii) how to achieve network-wide synchronization with high accuracy, minimal overhead, and most importantly, bounded delay, and (iii) how to integrate the synchronization algorithm with the TDM MAC protocol state machine using minimal hardware resources. We apply our design framework to our platform and evaluate the resulting TDM MAC protocol through controlled experiments in a wireless mesh testbed. The results demonstrate the protocol's ability to provide fairness and graceful performance degradation under packet losses and multi-hop traffic patterns that arise in mesh network deployments.

Community wireless mesh networks (WMNs) are increasingly being deployed for providing cheap, low maintenance Internet access. For the successful adoption of WMNs as a last-mile technology, we argue that a guarantee of per-client fairness is critical. Specifically, WMNs should support a bitrate-for-bucks service model similar to other popular access technologies such as Cable/DSL.We analyze the effectiveness of both off-the-shelf and theoretically optimal approaches towards providing such a service. We propose the APOLLO system that outperforms both these approaches.APOLLO seamlessly integrates three synergistic components: theory-guided service planning and subscription, rate-based admission control to enforce the planned service, and a novel distributed light-weight fair scheduling scheme to deliver the admitted traffic. We evaluate APOLLO using simulations and testbed experiments.

A major task of a wireless sensor network is energy-efficient, timely, and robust dissemination of sensor readings back to the sink node. The AIDA project [1] aims to create a new sensor network architecture, that uses antenna arrays of the distributed sensor nodes to create directional radio waves that can reach order-of-magnitude farther than individual antennas of the sensor nodes. This new network architecture, based on collaborative beamforming, drastically changes many aspects of the conventional networking stack. As part of the AIDA project, we are designing new energyefficient MAC and routing protocols for the new, distributed beamforming physical layer, which effectively involves “manysensors-to-many-sensors” for a single data packet transmission. 1. BACKGROUND

Many important applications in wireless mesh networks require reliable multicast communication. Previously, forward error correction (FEC) techniques have been proved successful for providing reliability in the Internet, as they avoid the control packet implosion and scalability problems of ARQ-based protocols. In this paper, we examine if FEC can be equally efficient wireless mesh networks. We implement four reliable schemes initially proposed for wired networks on top of ODMRP, a popular unreliable multicast routing protocol for wireless networks. We compare the performance of the four schemes using extensive simulations. Our results show that the use of pure FEC can offer significant improvements in terms of reliability, increasing PDR up to 100% in many cases, but it can be very inefficient regarding the number of redundant packets it transmits. Moreover, a carefully designed hybrid protocol, such as RMDP, can maintain the same high level of reliability while improving the efficiency by up to 38% compared to a pure FEC scheme.

The stationary nature of nodes in a mesh network has shifted the main design goal of routing protocols from maintaining connectivity between source and destination nodes to finding high-throughput paths between them. In recent years, numerous link-quality-based routing metrics have been proposed for choosing high-throughput paths for unicast protocols. In this paper we study routing metrics for high-throughput tree or mesh construction in multicast protocols. We show that there is a fundamental difference between unicast and multicast routing in how data packets are transmitted at the link layer, and accordingly there is a difference in how the routing metrics for each of these primitives are designed. We adapt certain routing metrics for unicast for high-throughput multicast routing and propose news ones not previously used for high-throughput. We then study the performance improvement achieved by using different link-quality-based routing metrics via extensive simulation and experiments on a mesh network testbed, using ODMRP as a representative multicast protocol. Our testbed experiment results show that ODMRP enhanced with linkquality routing metrics can achieve up to 17.5% throughput improvement as compared to the original ODMRP.

Wireless mesh networks (WMNs) have been proposed as an effective solution for ubiquitous last-mile broadband access. Three key factors that affect the usability of WMNs are high throughput, cost-effectiveness, and ease of deployability. In this paper, we propose DMesh, a WMN architecture that combines spatial separation from directional antennas with frequency separation from orthogonal channels to improve the throughput of WMNs. DMesh achieves this improvement without inhibiting cost-effectiveness and ease of deployability by utilizing practical directional antennas that are widely and cheaply available (e.g., patch and yagi) in contrast to costly and bulky smart beamforming directional antennas. Thus, the key challenge in DMesh is to exploit spatial separation from such practical directional antennas despite their lack of electronic steerability and interference nulling, as well as the presence of significant sidelobes and backlobes. In this paper, we study how such practical directional antennas can improve the throughput of a WMN. Central to our architecture is a distributed, directional channel assignment algorithm for mesh routers that effectively exploits the spatial and frequency separation opportunities in a DMesh network. Simulation results show that DMesh improves the throughput of WMNs by up to 231% and reduces packet delay drastically compared to a multiradio multichannel omni antenna network. A DMesh implementation in our 16-node 802.11b WMN testbed using commercially available practical directional antennas provides transmission control protocol throughput gains ranging from 31% to 57%