Wireless Communication Lab:
Wideband Space-Time Code Massive MIMO systems:
Jason Quinton investigating new transmission schemes in wireless communications to achieve increased throughput and signal reliability. General topics of interest include OFDM modulation, Massive MIMO, and coding data in space and time(STC). Using MatLab, successfully created and simulated a space-time coded wideband massive MIMO (STCWM-MIMO) system which performs significantly better than an uncoded wideband massive MIMO system. Future research includes investigating a new multicarrier modulation scheme and it's potential benefits and restrictions.
MIMO Space-Time Code Design for Wireless Communication:Jeremy Ice is working with the VLSI circuit design team to come up with an optimal VLSI architecture for space-time coding techniques used in next genereation of wireless communication systems. In this research, the focus is on designing of a MIMO-OFDM physical layer that follows the IEEE standard 802.11n. Different VLSI techniques are examined and analyzed to obtain an efficient low-power architecture. In our experiments, we propose a pipelined architecture to the baseline MIMO-OFDM physical layer. By exploiting the dynamic reconfiguration, the proposed MIMO-OFDM system can adapt to various operating modes. The team also present experimental results and analysis regarding dynamic reconfiguration.
Human Body Localization and Tracking Using RF Wireless Signals:
Emerson Argueta is working on researching and contributing to the field of research dealing with localization of humans through RF wireless signals, specifically in developing an imaging model for such reflected signals so that they can be analyzed through computer vision methods. By transmitting RF signals and receiving them, it is possible to calculate the distance of their reflection. Because human bodies reflect RF these wireless signals, it is possible, through various techniques, to localize a human. The research will be focused on RF wireless signals as a mode for sensing. The research will encompass tracking multiple subjects in indoor environments with higher resolution, methods of creating a higher resolution skeletal image of subjects using super-resolution techniques, detecting human vital signs, and using machine learning algorithms to help identify different subjects through data acquired by RF wireless sensing.
Information Processing and Learning for Dynamic and Social Networks:
Taylor Redden is researching estimation and information processing over dynamic networks with time varying topologies. The purpose of the research is to find algorithms that accurately and efficiently used in these networks. The research tasks include modeling and inference for information diffusion and rumor spreading, social media computing and networking, data mining, machine learning, and statistical inference frameworks and algorithms for handling big data from social networks. The second line of this research involves in attribution models for marketing and advertising, trend prediction, recommendation systems and crowd sounding.
VLSI Implemenataion of Detection Techniques in Massive MIMO Systems:
Ian Ives is working on detection and estimation techniques for Multiple Input Multiple Output (MIMO) systems. This will include MIMO systems working with two transmit and receive antennas to massive MIMO systems with hundreds of antennas. MIMO detection and estimation falls into two categories of boosting capability: devising a better algorithm and reducing computational complexity. Devising a better algorithm involves balancing reducing error a signal produces after being transmitted through a wireless channel and reducing the strain a device will receive by trying to follow that algorithm. Reducing computational complexity can be grouped with making a better algorithm, but are not the same. Reducing computational complexity involve activities such as lowering the number of calculations a device would have to make and doing those calculations on a simpler/smaller circuit.