Projects

Project 1: Outdoor Millimeter Wave Systems for 5G Urban Cellular: Design and Analysis

• Description: The existing 2/3/4G cellular networks are operated on the congested sub-6 GHz spectrum bands. In these bands, spectrum efficiency has been critically improved by utilizing sophisticated signal processing and resource schedule techniques. Nevertheless, achieving a gigabits-per-second date rate, suitable for the upcoming traffics such as virtual and augment reality, is quite challenging due to the limited bandwidth. This motivates the industry and academia to shift operating frequencies upward to the millimeter wave bands to accommodate more broadband spectrum.
• Our work: To evaluate the millimeter wave network performance, we develop an outdoor urban millimeter wave network simulator. The main work includes: (1) Develop a high-fidelity ray tracer to emulate the millimeter wave channel; (2) Implement the beam alignment and hybrid precoding algorithms for millimeter wave link transmission; (3) Design the interference management algorithm for millimeter wave networks. Video demos of the simulator are given at the below.

Project 2: Base Station Deployment and Dynamic User Association for 5G Millimeter Wave Cellular Networks

• Description: Due to the high pathloss propagation, millimeter wave base stations are likely to form small-cell networks. To provide seamless coverage, the small cells should be densely deployed. The network densification will incur significant deployment and maintenance costs. An intuitive cost-efficient base station deployment strategy is to minimize the number of deployed millimeter wave base station while guaranteeing the user equipments' quality-of-service requirements.
• Our work: Considering that the user equipment locations at the stage of base station deployment is random, we investigate the millimeter wave base station deployment from two aspects: (1) When the random user equipment placement can be emulated by a Poisson point process, we use the theories of stochastic process to formulate and solve the base station deployment problem; (2) When the random user equipment placement can be measured by collecting numerous realizations, we employee the scenario sampling approach to formulate and solve the base station deployment problem.