Munish Goyal

Optimal Cross-Layer Scheduling of Transmissions Over a Fading Multiaccess Channel

We consider the problem of several users transmitting packets to a base station, and study an optimal scheduling formulation involving three communication layers, namely, the medium access control, link, and physical layers. We assume Markov models for the packet arrival processes and the channel gain processes. Perfect channel state information is assumed to be available at the transmitter and the receiver. The transmissions are subject to a long-run average transmitter power constraint. The control problem is to assign power and rate dynamically as a function of the fading and the queue lengths so as to minimize a weighted sum of long run average packet transmission delays.

Modeling the impact of review dynamics on utility value of a product

Manufacturer-provided specifications often do not provide a true picture of the utility value of a product. A product’s true assessed value is the result of consumer opinion often conveyed via word of mouth. The increasing popularity of social media has led to the inevitable integration of the social platform with e-commerce sites where consumers share their opinions on products and prospective buyers seek the opinion of their peers before making a purchase. The influencing power of these social platforms has led to researchers mining these opinions and utilizing them to assess the value of the product. Consumer opinion can vary greatly and is dependent on several factors such as when the product is launched into the market, what competitors are offering and how their product is faring over time, etc. Hence, the assessed value of a product is subject to significant dynamism which if modeled accurately, can provide several business insights. Experience has taught us that accurately capturing the time at which opinions are expressed and identifying the attributes that influence these opinions play an important role in determining assessed value; our model aims to capture this information accordingly. Our experiments are based on large-scale review sets (approximately 30,000 reviews) collected from real-world portals such as Amazon, Mouthshut and IMDB. Validation using this real-world data confirms the superiority of our model. We demonstrate that the utility value when modeled as a function of time on the most valued attributes, provides business insights.

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.