Mukundan Madhavan

Quantifying multiplexing gains in a Wireless Network Cloud

The Wireless Network Cloud (WNC) [1] is a novel network architecture where wireless base-stations are implemented as software modules and multiple base-stations are consolidated to a single centralized computing platform. Due to the time-varying and random nature of base-station traffic, consolidation leads to multiplexing of statistically-varying basestation loads on a common hardware platform. In turn, this can lead to significant hardware reduction in the consolidated platform when compared to the distributed network. This paper represents the first analysis of such consolidation gain. Through traffic simulation experiments, we quantify the extent and variation of this multiplexing gain in a WiMAX base-station network in different traffic conditions. We show experimentally, that the obtained gain increases linearly with network size (number of base-stations). Further, we also show that the consolidation gain is higher when the consolidated base-stations face higher traffic intensity.

Adapting cellular networks to whitespaces spectrum

TV Whitespaces, recently opened up by the Federal Communications Commission (FCC) for unlicensed use, are seen as a potential cellular offload and/or standalone mechanism, especially in dense metros where the demand for throughput is high. In this paper, we use real data collected from whitespaces databases to empirically demonstrate features unique to whitespaces-power-spectrum tradeoff and spatial variation in spectrum availability. From this study, we conclude the need for whitespaces-specific adaptations to cellular networks so as to be able to extract maximum throughput and guarantee reliability. To tackle the effects of the power-spectrum tradeoff, we propose a novel base-station design that specifically uses low-power transmitters as a means to maximize throughput. This design co-locates and networks together many low-powered mode-I devices to act as a multiple-antenna array. We estimate the size of the array required to meet typical rate targets, and show that the array design significantly outperforms traditional designs in terms of throughput for a given cost. We then turn our attention to spatial variability and study its impact on the problem of locating base stations in a whitespaces network. Here, we propose spectrum-aware placement algorithms for whitespaces, which account for this spatial variability along with key parameters like user density. We show that such algorithms clearly outperform traditional placement algorithms and improve network coverage in this band.

Async: De-congestion and yield management in cellular data networks

We design and implement a novel system called Async, which enables a mobile network operator (MNO) to efficiently manage the growth of mobile data by leveraging the delay-elastic nature of certain applications and the price-sensitive nature of certain users. Specifically, Async introduces an alternate “asynchronous” content-delivery paradigm for heavy content (e.g., videos), and facilitates an MNO to negotiate with users a delay in delivery in exchange for appropriate incentives. The MNO uses the negotiated delays to actively manage Async flows to reduce congestion and improve the quality-of-experience (QoE) of both delayed and regular flows. We show that in comparison to state-of-the-art, Asyncs network-based flow management enhances QoE for more than 30% of the regular flows, with up to 60% improvement in per-flow QoE metric, while still meeting the negotiated delivery times of 95% of the delayed flows. Async also lowers the delivery times of delayed flows by ∼67% and significantly increases robustness to traffic unpredictability. Our design is robust to disconnections and does not require any modifications to existing network infrastructure and protocols. Our prototype deployment (using Apaches mod_proxy and an Android app) on live networks confirms Asyncs efficacy in meeting EDTs for diverse deployment scenarios.

On Exploiting degrees-of-freedom in whitespaces

TV Whitespaces, recently opened up by the FCC for unlicensed use by wireless devices, are seen as a potential cellular offload solution, especially in dense metros. However, under the new database-driven guidelines, there are typically very few whitespace bands available in such dense metros to a high-powered fixed device, which plays the role of a cellular base station in whitespaces. To address the lack of degrees-of-freedom (DoF) with this traditional architecture of one high-powered serving device, we propose a novel base station design that co-locates and networks together many low-powered devices to act as a multiple-antenna array. Lower-powered whitespace devices have access to more spectral DoF, a property that is unique to whitespaces. In the first part of the paper, we solve an array design problem where we estimate the size of the array required to meet long-term (worst-case) throughput targets. Using extensive simulations, we show that by effectively exploiting both spatial and spectral DoF, the array design outperforms the traditional design in most network conditions. Specifically, the proposed design can support throughputs of the order of a WiMAX cell running applications such as high-definition television. In the second part of the paper, we turn our attention to the operational aspects of such a design. Recognizing that the proposed array can potentially contain hundreds of elements, we propose a dynamic ON-OFF power control algorithm that operates in conjunction with the MaxWeight data scheduling algorithm and responds to the current network state - queues and channels - of the system, thus making the system power-efficient.

SERA: A hybrid scheduling framework for M2M transmission in cellular networks

Trends show that machine-to-machine (M2M) devices are going to grow by orders of magnitude, far surpassing the number of mobile devices. This unprecedented scale and the fact that M2M traffic typically consists of many small-sized transmissions make the data and signaling overhead of introducing M2M traffic into cellular networks a big concern. Fortunately, it is possible to exploit certain unique characteristics of M2M traffic, like periodicity and delay tolerance in its scheduling, to alleviate these concerns. In this paper, we propose SERA - a two-level Scheduled Randomization framework, which does precisely this, and efficiently integrates M2M traffic into cellular networks. Broadly, SERA consists of (i) a central controller that defines certain coarse-level transmission parameters to govern M2M traffic in the next scheduling period and (ii) a simple distributed randomized algorithm at each M2M device that governs fine-grained transmission decisions within the period. Using experiments and analyses, we show that compared to existing techniques for M2M traffic management, SERA can lower peak traffic load by 30-40%, bring down the total time spent under congestion by 30-40%, and that these gains are robust to errors in traffic prediction.

Case Studies in Managing Traffic in a Developing Country with Privacy-Preserving Simulation as a Service

Simulation is known to be an effective technique to understand and manage traffic in cities of developed countries. However, in developing countries, traffic management is lacking due to a wide diversity of vehicles on the road, their chaotic movement, little instrumentation to sense traffic state and limited funds to create IT and physical infrastructure to ameliorate the situation. Under these conditions, in this paper, we present our approach of using the Megaffic traffic simulator as a service to gain actionable insights for two use-cases and cities in India, a first. Our approach is general to be readily used in other use cases and cities, and our results give new insights: (a) using demographics data, traffic demand can be reduced if timings of government offices are altered in Delhi, (b) using a mobile companys Call Data Record (CDR) data to mine trajectories anonymously, one can take effective traffic actions while organizing events in Mumbai at local scale.