Clayton Shepard

Argos: practical many-antenna base stations

Multi-user multiple-input multiple-output theory predicts manyfold capacity gains by leveraging many antennas on wireless base stations to serve multiple clients simultaneously through multi-user beamforming (MUBF). However, realizing a base station with a large number antennas is non-trivial, and has yet to be achieved in the real-world. We present the design, realization, and evaluation of Argos, the first reported base station architecture that is capable of serving many terminals simultaneously through MUBF with a large number of antennas (M >> 10). Designed for extreme flexibility and scalability, Argos exploits hierarchical and modular design principles, properly partitions baseband processing, and holistically considers real-time requirements of MUBF. Argos employs a novel, completely distributed, beamforming technique, as well as an internal calibration procedure to enable implicit beamforming with channel estimation cost independent of the number of base station antennas. We report an Argos prototype with 64 antennas and capable of serving 15 clients simultaneously. We experimentally demonstrate that by scaling from 1 to 64 antennas the prototype can achieve up to 6.7 fold capacity gains while using a mere 1/64th of the transmission power.

LiveLab: measuring wireless networks and smartphone users in the field

We present LiveLab, a methodology to measure real-world smartphone usage and wireless networks with a reprogrammable indevice logger designed for long-term user studies. We discuss the challenges of privacy protection and power impact in LiveLab and offer our solutions. We present an iPhone 3GS based deployment of LiveLab with 25 users intended for one year. Early results from the data collection so far highlight the unique strengths and potential of LiveLab. We have two objectives in this position paper. First, we demonstrate the feasibility and capability of LiveLab. By sharing our experience, we seek to advocate LiveLab as a network and user measurement methodology. Second, we present our preliminary findings, and seek feedback from the community regarding what data to collect.

Characterizing web use on smartphones

The current paper establishes empirical patterns associated with mobile internet use on smartphones and explores user differences in these behaviors. We apply a naturalistic and longitudinal logs-based approach to collect real usage data from 24 iPhone users in the wild. These data are used to describe smartphone usage and analyze revisitation patterns of web browsers, native applications, and physical locations where phones are used. Among our findings are that web page revisitation through browsers occurred very infrequently (approximately 25% of URLs are revisited by each user), bookmarks were used sparingly, physical traversing patterns mirrored virtual (internet) traversing patterns and users systematically differed in their web use. We characterize these differences and suggest ways to support users with enhanced design of smartphone technologies and content.

Exploring iPhone usage: the influence of socioeconomic differences on smartphone adoption, usage and usability

Previous studies have found that smartphone users differ by orders of magnitude. We explore this variability to understand how users install and use native applications in ecologically-valid environments. A quasi-experimental approach is applied to compare how users in different socio-economic status (SES) groups adopt new smartphone technology along with how applications are installed and used. We present a longitudinal study of 34 iPhone 3GS users. 24 of these participants were chosen from two carefully selected SES groups who were otherwise similar and balanced. Usage data collected through an in-device programmable logger, as well as several structured interviews, identify similarities, differences, and trends, and highlight systematic differences in smartphone usage. A group of 10 lower SES participants were later recruited and confirm the influence of SES diversity on device usage. Among our findings are that a large number of applications were uninstalled, lower SES groups spent more money on applications and installed more applications overall, and the lowest SES group perceived the usability of their iPhones poorly in comparison to the other groups. We further discuss the primary reasons behind this low score, and suggest design implications to better support users across SES brackets.

ArgosV2: a flexible many-antenna research platform

Many-antenna base stations are a rapidly growing field in wireless research. A plethora of new theoretical techniques have been recently proposed for many-antenna base stations and networks. However, without experimental validation, it is difficult or impossible to predict the practicality and performance of these techniques in real hardware, under complex, rapidly varying, real-world conditions. Indeed, there is a significant demand for a flexible many-antenna research platform which supports rapid prototyping and validation of new massive-MIMO techniques. Leveraging our experience building Argos, a 64-antenna base station prototype, we have designed and built ArgosV2, a compact, powerful, and scalable many-antenna research platform based on WARP. In addition to the physical hardware and mechanical design, we are developing a software framework, ArgosLab, which will provide synchronization and channel estimation, greatly reducing the development effort for a wide range of massive-MIMO techniques. ArgosV2 is intended to provide ultimate scalability and programmability for experimental massive-MIMO research. The modular architecture and real-time capability of ArgosV2 can support up to 100s of base station antennas and 10s of users with streaming applications. For our demonstration, we will unveil a 96-antenna base station which supports real-time streaming applications to 32 users simultaneously.

Realizing the full potential of PSM using proxying

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.

An empirical analysis of smartphone personalisation: measurement and user variability

The present report is an empirical analysis of smartphone personalisation. We collected data from two groups of users to measure how they adapt the content, interface and physical appearance of their devices. This user-driven personalisation is measured with a simple heuristic approach to quantify the behaviour. Using these scores, we explore how users differ from each other in how they personalise their smartphones with a focus on gender differences, usability and device usage in the wild. Among our findings are that not all users personalise their smartphones, females and males personalise their iPhones differently, and those who personalised their phones more tended to rate it as more usable. The users who personalised more also used their device for greater periods of time on a broader range of applications. For instance, individuals who adapted their iPhones to a greater degree also accessed the Web more often and spent more time browsing once it was accessed. We conclude with a discussion of possible factors underlying the large user diversity of smartphone personalisation found in this research.

Practical Context Awareness: Measuring and Utilizing the Context Dependency of Mobile Usage

Context information brings new opportunities for efficient and effective applications and services on mobile devices. A wide range of research has exploited context dependency, i.e. the relations between context(s) and the outcome, to achieve significant, quantified, performance gains for a variety of applications and services. These works typically have to deal with the challenges of multiple context sources leading to a sparse training dataset, and the challenges of energy hungry context sensors. Often, they address these challenges in an application specific and ad-hoc manner. We liberate mobile application designers and researchers from these burdens by providing a methodical approach to these challenges. In particular, we 1) define and measure the context-dependency of three principal types of mobile usage (visited websites, phone calls, and app usage) in an application agnostic yet practical manner, providing insight into the performance of potential application. 2) Address the challenge of data sparseness when dealing with multiple context sources in a systematic manner. 3) Present SmartContext to address the energy challenge by automatically selecting among context sources while ensuring a minimum accuracy for each estimation. Our analysis and findings are based on one year of usage and context traces collected in real-life settings from 24 iPhone users. We present findings regarding the context dependency of three types of mobile usage from 24 users, yet our methodology and the lessons we learn can be readily extended to other types of usage as well as system resources. Our findings guide the development of context aware systems, and highlight the challenges and expectations regarding the context dependency of mobile usage.

NoShake: Content stabilization for shaking screens of mobile devices

Consumer electronics and mobile devices intended for pervasive applications are often subject to shaking that makes their screen difficult to read. To address this usability challenge, we present NoShake, a system for screen content stabilization. NoShake utilizes a single accelerometer, now present in numerous consumer electronics and mobile devices. The core of NoShake is a physics inspired model that dynamically compensates for the device shaking by shifting the screen content opposite the direction of the shake. The model is efficient, parametric, and can be fine tuned based on shaking pattern detection. We implement a prototype of NoShake on an Apple iPhone and conduct user studies in a number of scenarios, which highlight the strengths as well as limitations of NoShake in coping with shaking devices.

Getting real: a naturalistic methodology for using smartphones to collect mediated communications

This paper contributes an intentionally naturalistic methodology using smartphone logging technology to study communications in the wild. Smartphone logging can provide tremendous access to communications data from real environments. However, researchers must consider how it is employed to preserve naturalistic behaviors. Nine considerations are presented to this end. We also provide a description of a naturalistic logging approach that has been applied successfully to collecting mediated communications from iPhones. The methodology was designed to intentionally decrease reactivity and resulted in data that were more accurate than self-reports. Example analyses are also provided to show how data collected can be analyzed to establish empirical patterns and identify user differences. Smartphone logging technologies offer flexible capabilities to enhance access to real communications data, but methodologies employing these techniques must be designed appropriately to avoid provoking naturally occurring behaviors. Functionally, this methodology can be applied to establish empirical patterns and test specific hypotheses within the field of HCI research. Topically, this methodology can be applied to domains interested in understanding mediated communications such as mobile content and systems design, teamwork, and social networks.