Ghaith Hattab

Graduate Research Assistant at UCLA.

Research Interests

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.

Summary of Current and Previous Projects

I'm 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.

Spectrum sharing techniques for cellular massive IoT

Previous generations of cellular networks have been optimized for human-type traffic, with earlier generations focusing on voice-only applications while more recent ones, e.g., 3G and 4G, being tailored for throughput-hungry applications such as streaming. Although the fifth generation cellular network (5G-NR) is set to continue the evolution of enhanced mobile broadband, it is envisioned to transcend human-type applications by enabling two other use cases: ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC). Both new use cases will expand on recent applications and opportunities enabled by the Internet-of-things (IoT). Indeed, URLLC is expected to revolutionize industrial applications, getting us closer to realizing what is commonly known as Industry 4.0, whereas mMTC is expected to enable connectivity for millions of sensors and machines that can be utilized across many vertical sectors, enabling smarter cities, efficient farming, etc.
In this project, we focus on the coexistence of massive IoT (or mMTC) with legacy cellular users (UEs). We analyze the existing transmission protocols that are used by 3GPP, including the separation of resource blocks and access class barring, and how they affect the performance of UEs and IoT devices. We show that existing solutions don't scale well with the density of IoT devices due to increased channel congestion, increased interference, or resource splitting. To this end, we consider using data aggregators to act as a middle layer between IoT devices and the cellular base stations (gNBs). Specifically, we use UAVs, or drones, to collect data from IoT devices and aggregate them to the cellular network, where a time-division duplexing (TDD) protocol is proposed so that IoT devices, drones, and UEs share the same spectrum. To ensure a harmonious coexistence with UEs, we develop a stochastic optimization framework that maximizes the energy-efficiency of IoT devices, defined as the ratio of rate to the power consumption, subject to interference constraints on UEs. We study the convexity of the optimization problem and develop algorithms to solve it. We show that our framework significantly improves the energy-efficiency of IoT devices, compared to transmitting data at maximum power, with minimal degradation on the UEs' spectral efficiency. 
  • G. Hattab and D. Cabric, "Energy-Efficient Massive IoT Shared Spectrum Access over UAV-enabled Cellular Networks", submitted to IEEE Trans. on Wireless Commun., Aug. 2018 (link).
  • G. Hattab and D. Cabric, "Performance Analysis of Uplink Cellular IoT Using Different Deployments of Data Aggregators", to appear in IEEE GLOBECOM 2018.
  • G. Hattab and D. Cabric, "Energy-Efficient Massive Cellular IoT Shared Spectrum Access via Mobile Data Aggregators", in IEEE WiMob'17, Oct. 2017 (link).

Revisiting spectrum sharing techniques for unlicensed massive IoT

Massive Internet-of-Things (IoT) communications aim to provide connectivity to sensors and machines at a very large scale. Many technologies have emerged to realize massive IoT applications, with most of them using unlicensed spectrum due to its cost-effectiveness. Nevertheless, unlicensed access elevates the need for spectrum sharing techniques for fair coexistence with incumbent networks. More critical to the massive IoT market is the need for connecting a massive number of low-cost devices, which requires identifying many narrow band channels at a fine spectral resolution and then reusing these channels aggressively over space.

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. 

  • G. Hattab and D. Cabric, "Spectrum Sharing Protocols Based on Ultra-Narrowband Communications for Unlicensed Massive IoT", to appear in IEEE DySPAN 2018.
  • G. Hattab and D. Cabric, "Distributed Wideband Spatio-Spectral Sensing for Unlicensed Massive IoT Communications", to appear in IEEE GLOBECOM 2018.
  • G. Hattab and D. Cabric, “5G Unlicensed Spectrum Access for Massive Machine Type Communications Enabled by Distributed Wideband Spectrum Sensing", in IEEE TCCN Newsletter, Nov. 2017 (link).

Load Balancing and Interference Management for 5G Networks

With the proliferation of wireless devices and the advent of bandwidth hungry applications such as video streaming, cloud-based technologies, etc., incremental improvements to existing networks and architectures such as 4G are insufficient to meet the projected data demands in the next five years. This has called for paradigm shifts in next generation networks, i.e., 5G, and particularly, the ongoing evolution towards very dense and unplanned deployment of low-power small-cell base stations (BSs) of various types, commonly known as HetNets.

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.

  • G. Hattab and D. Cabric, “Coverage and Rate Maximization via User Association in Multi-antenna HetNets", in IEEE Trans. on Wireless Commun., Aug. 2018 (link).
  • G. Hattab and D. Cabric, “Rate-based Cell Range Expansion for Downlink Massive MIMO Heterogeneous Networks", in IEEE Wireless Commun. Letters, Nov. 2017 (link).
  • G. Hattab and D. Cabric, "Long-term Rate-based User-centric Association for Downlink Multi-antenna HetNets", in IEEE ICC, May 2018 (link).
  • G. Hattab and D. Cabric, "Joint Resource Allocation and User Association in Multi-Antenna Heterogeneous Networks", in IEEE GLOBECOM, Dec. 2016 (link).
  • G. Hattab and D. Cabric, "Inter-tier Interference Mitigation in Multi-Antenna HetNets: A Resource Blanking Approach", in IEEE GLOBECOM, Dec. 2016 (link).