My research interests lie in the areas of wireless communications and networking, with emphasis on spectrum sharing and coexistence of wireless systems. I leverage tools like optimization, signal processing, stochastic processes, and communication theory to solve wireless communications challenges, particularly those pertaining massive IoT connectivity, ultra-reliable communications for industrial IoT, and dynamic spectrum access.
currently working on the development of spectrum sharing techniques for massive IoT communications. Specifically, we investigate the coexistence of IoT devices with legacy cellular users, over the the licensed bands, and with other wireless networks operating in the unlicensed bands. In addition to my work on enabling massive IoT connectivity, I have worked on critical problems pertaining interference management and load balancing in dense 5G heterogeneous networks (5G HetNets).
During my internship at Qualcomm Research, I have had the opportunity to work on algorithm development and protocol design for coordinated multipoint (CoMP) to enable ultra-reliable low-latency communications for industrial IoT use cases. At Nokia Bell Labs, I have worked on the coexistence of 5G networks with fixed stations operating at 70GHz/80GHz and with satellite earth stations operating at 3.7-4.2GHz.
During my master's degree, my research has centered around dynamic spectrum access. Specifically, we investigated the challenges and limitations of state-of-the-art multiband spectrum sensing techniques, proposed a pragmatic cooperative sensing framework for multiband sensing, and investigated the benefits of network-wide based reconfiguration in both centralized and distributed cognitive radio networks. In addition, we have proposed new sensing algorithms, including an enhanced pilot-tone aided detector that overcomes imperfect phase synchronization and noise power uncertainty and a confidence-based generalized combining scheme for cooperative sensing.
During my bachelor's degree, I have worked on the design of an underwater wireless sensor network (WSN). We have developed a realistic channel model to understand the path loss in underwater environments, and designed a prototype that enables WSNs to use electromagnetic waves as means of communications.
Finally, I have worked on a personal project, where I have written a book on the fundamentals of signal detection and estimation.
In this project, we overview recent unlicensed-based technologies for IoT networks. Specifically, we focus on one of the variants of low-power wide-area (LPWA) networks, the ultra-narrowband (UNB) network. In UNB networks, communication is done via extremely narrow signals (e.g., Sigfox uses 100Hz in Europe and 600Hz in the US). Furthermore, simple ALOHA-like protocols are used, with asynchronous access, i.e., an IoT device transmits a signal repeatedly at any time within a specific band, hoping at least one base station (BS) would listen to at least one of the messages. We propose a framework, using stochastic geometry, to model and analyze the coverage performance and transmission capacity of such networks, in the presence of incumbent networks, e.g., LoRa or WiFi. We further present enhanced access protocols that realize UNB communications over multiple bands instead of just one. We show that UNB networks can support hundreds of thousands of machines using a small number of BSs.
Although UNB networks are simple and scalable, they are tailored to very low-rate applications, and they operate in bands where sensing or listen-before-talk (LBT) is not mandated. To use other bands, where spectrum is wider, e.g., 5GHz, LBT is required in some parts of the world. For instance, MulteFire is a technology that promises to provide cellular NB-IoT (or LTE-M) operation over the unlicensed spectrum. We show that such technology (and others that rely on non-cooperative LBT) can be suitable for indoor applications, but not for city-wide scenarios. To provide massive IoT connectivity, we propose an architecture that enables the network to learn the occupancy of a wideband spectrum (~500MHz) over fine spectral resolution (~180KHz) and fine spatial resolution (~200m). The architecture consists of two components. The first one is an assignment scheduler that assigns each BS a subset of the spectrum to sense, such that each subset is sensed by a given number of BSs to ensure reliable sensing in fading channels. The second component is a distributed sensing algorithm that allows the BS to sense only a subset of the spectrum, shares its measurements only with its neighbors, yet obtain a global view of the entire spectrum through intelligent occupancy information dissemination. We validate the performance of the proposed architecture via simulations and a case study that emulates deploying 1e5 sensors in the public parks of NYC, and studying how many of these sensors can be supported, coexisting with more than 2000 public outdoor WiFi access points.
Small cells can significantly enhance network’s throughput, but there are several key challenges associated with the deployment of HetNets such as load-balancing and the high interference. For instance, low-power BSs can be overshadowed by the high transmit power of the macro BS, rendering existing user association techniques such as the max-power user association impractical due to the load-imbalance it causes in the network. Similarly, co-channel deployment of these cells prohibitively increases the interference, degrading the quality-of-service especially for users located at cell edges.
We propose user association
policies that either maximize the user’s throughput or coverage by balancing the load
for different network settings. The proposed policies are user-centric, i.e., they don't require network-wide coordination. Furthermore, we propose different implementations of the proposed policies including cell-range expansion. In addition, we study interference not only via
the allocation of time-frequency resources but also via the allocation of
spatial resources based on the premise that massive MIMO will be an integral
technology in 5G. The proposed policies are validated using realistic 3GPP channels, showing tangible improvements in rate and coverage.