Grant: Research Tools and Instruments

Motivated by the increasing demands for better services in the fifth-generation (5G) wireless networks such as higher transmission rate, link reliability, ubiquitous access to internet etc, the multi-input multi-output (MIMO) system operating at millimeter wave frequencies (30GHz-300GHz) has recently received a great deal of attention. However, MIMO (especially when the number of antennas increases) has been constrained by the need of extensive channel state information for all links and transmitting pilot signals in the uplink for channel estimation becomes a resource-consuming and challenging task. Moreover, the sparse channel under millimeter wave frequencies presents a unique concern in the field of signal processing and communication. As such, it is imperative to develop intelligent signal processing techniques for millimeter wave enabled MIMO or massive MIMO systems to be applied in upcoming 5G networks. My current Discover project is focused on the development of robust and energy efficient signal processing techniques for multi-user massive MIMO systems. The main objective of this research is to maximize the transmission reliability, spectral usage and energy efficiency of massive MIMO networks under realistic channel conditions. However, the algorithms developed under the Discovery project are largely academic due to the lack of real channel measurements and real-time testbed. Also, the non-linear power amplifiers, practical antennas, and distortions introduced by the feed lines could not be accurately modeled. As such, in order to develop practically applicable transmission schemes and undertake experiments on a real propagation environment in millimeter MIMO communication, I wish to acquire the PicoSDR equipment along with the required user terminals and supporting components to facilitate and enhance my Discovery program as it will gear the designed algorithms toward real-time applications. The acquisition of the new equipment will also open up exciting different avenues for future research, such as developing sparse channel estimation techniques for millimeter wave MIMO and deep learning algorithms for MIMO cognitive radio (CR) networks using neural networks. The proposed equipment will provide exciting new avenues for HQP training involving a hands-on approach compared to the traditional simulation based training environment. With the PicoSDR testbed, HQPs will be able to optimize their algorithms under real indoor propagation environments. Moreover, the FPGA implementation will enable fine tuning the algorithms with respect to practical hardware limitations of antennas and radio links. All these together will promote industrial application oriented research and the chance of the proposed algorithms being patented and implemented in real-world systems.