Mobile network traffic is set to explode in our near future, driven by the growth of bandwidth-hungry media applications. Current capacity solutions, including buying spectrum, WiFi offloading, and LTE picocells, are unlikely to supply the orders-of-magnitude bandwidth increase we need. In this paper, we explore a dramatically different alternative in the form of 60GHz mmwave picocells with highly directional links. While industry is investigating other mmwave bands (e.g. 28GHz to avoid oxygen absorption), we prefer the unlicensed 60GHz band with highly directional, short-range links (∼100m). 60GHz links truly reap the spatial reuse benefits of small cells while delivering high per-user data rates and leveraging efforts on indoor 60GHz PHY technology and standards. Using extensive measurements on off-the-shelf 60GHz radios and systemlevel simulations, we explore the feasibility of 60GHz picocells by characterizing range, attenuation due to reflections, sensitivity to movement and blockage, and interference in typical urban environments. Our results dispel some common myths, and show that there are no fundamental physical barriers to high-capacity 60GHz outdoor picocells. We conclude by identifying open challenges and associated research opportunities.
We are facing an increasingly difficult challenge in spectrum management: how to perform real-time spectrum monitoring with strong coverage of deployed regions. Today's spectrum measurements are carried out by government employees driving around with specialized hardware that is usually bulky and expensive, making the task of gathering real-time, large-scale spectrum monitoring data extremely difficult and cost prohibitive. In this paper, we propose a solution to the spectrum monitoring problem by leveraging the power of the masses, i.e. millions of wireless users, using lowcost, commoditized spectrum monitoring hardware. We envision an ecosystem where crowdsourced smartphone users perform automated and continuous spectrum measurements using their mobile devices, and report the results to a monitoring agency in real-time. We perform an initial feasibility study to verify the efficacy of our mobile monitoring platform compared to that of conventional monitoring devices like USRP GNU radios. Results indicate that commoditized real-time spectrum monitoring is indeed feasible in the near future. We conclude by presenting a set of open challenges and potential directions for follow-up research.
How to distribute radio spectrum across network nodes is a critical problem in spectrum auctions and management. In this paper, we consider the problem of distributing spectrum using SINR-driven physical interference models. We propose Optimus, a new line of approximation algorithms that perform within a constant distance of min {2 α + 1, 10} from the optimum in terms of spectrum usage efficiency, where α ≥ 2 is the pathloss exponent. Different from conventional greedy solutions, Optimus applies a global optimization mechanism that transforms the spatial interference constraints into a set of linear constraints, reducing the original optimization into a linear/convex/separable programming problem. While linearization techniques have been applied in prior works, Optimus makes a new and important contribution by deriving a highly efficient constraint transformation applicable to general network configurations. Experiments using real network measurements and sophisticated propagation models show that Optimus outperforms existing solutions by 20-50% in spectrum utilization and is within 20% gap from the optimum. Optimus supports a wide variety of objective functions, and is applicable to many spectrum-driven applications such as spectrum auctions and spectrum admission control.
Contrary to prior assumptions, recent measurements show that data center traffic is not constrained by network bisection bandwidth, but is instead prone to congestion loss caused by short traffic bursts. Compared to the cost and complexity of modifying data center architectures, a much more attractive option is to augment wired links with flexible wireless links in the 60 GHz band. Current proposals, however, are severely constrained by two factors. First, 60 GHz wireless links are limited by line-of-sight, and can be blocked by even small obstacles between the endpoints. Second, even beamforming links leak power, and potential interference will severely limit concurrent transmissions in dense data centers. In this paper, we explore the feasibility of a new wireless primitive for data centers, 3D beamforming. We explore the design space, and show how bouncing 60 GHz wireless links off reflective ceilings can address both link blockage and link interference, thus improving link range and number of current transmissions in the data center.
Network Coordinate (NC) systems are efficient in scalable Internet latency estimation. While most of the focus has been put on how to distort Triangle Inequality Violation (TIV) in metric spaces to relieve the inaccuracy caused by it, TIV is a persistently and widely existing phenomenon on the Internet and thus should be embraced by future NC systems rather than being eliminated. Besides high accuracy, such an NC system can also provide the benefit of reducing the data transmission time by use of proper relay routes. With that in mind, we design an NC system with a hierarchical architecture, which is motivated by the natural idea of partitioning the three TIV links into different autonomous NC systems, in order to make as many as TIVs inherently embeddable in metric space. We implement and deploy our work, named Toread, on real Internet. Evaluation results show that Toread 's metric space can well characterize more than 60% TIVs, thus Toread is highly accurate (0.54 in Toread versus 1.06 in Pyxida at 90th percentile Relative Error) and effective in searching detour paths (succeeds in 58.2% cases).
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Mobile networking researchers have long searched for largescale, fine-grained traces of human movement, which have remained elusive for both privacy and logistical reasons. Recently, researchers have begun to focus on geosocial mobility traces, e.g. Foursquare checkin traces, because of their availability and scale. But are we conceding correctness in our zeal for data? In this paper, we take initial steps towards quantifying the value of geosocial datasets using a large ground truth dataset gathered from a user study. By comparing GPS traces against Foursquare checkins, we find that a large portion of visited locations is missing from checkins, and most checkin events are either forged or superfluous events. We characterize extraneous checkins, describe possible techniques for their detection, and show that both extraneous and missing checkins introduce significant errors into applications driven by these traces.
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