Soft-decision equalization techniques for frequency selective MIMO channels

Multi-input multi-output (MIMO) technology is an emerging solution for high data rate wireless communications. We develop soft-decision based equalization techniques for frequency selective MIMO channels in the quest for low-complexity equalizers with BER performance competitive to that of ML sequence detection. We first propose soft decision equalization (SDE), and demonstrate that decision feedback equalization (DFE) based on soft-decisions, expressed via the posterior probabilities associated with feedback symbols, is able to outperform hard-decision DFE, with a low computational cost that is polynomial in the number of symbols to be recovered, and linear in the signal constellation size. Building upon the probabilistic data association (PDA) multiuser detector, we present two new MIMO equalization solutions to handle the distinctive channel memory. With their low complexity, simple implementations, and impressive near-optimum performance offered by iterative soft-decision processing, the proposed SDE methods are attractive candidates to deliver efficient reception solutions to practical high-capacity MIMO systems. Motivated by the need for low-complexity receiver processing, we further present an alternative low-complexity soft-decision equalization approach for frequency selective MIMO communication systems. With the help of iterative processing, two detection and estimation schemes based on second-order statistics are harmoniously put together to yield a two-part receiver structure: local multiuser detection (MUD) using soft-decision Probabilistic Data Association (PDA) detection, and dynamic noise-interference tracking using Kalman filtering. The proposed Kalman-PDA detector performs local MUD within a sub-block of the received data instead of over the entire data set, to reduce the computational load. At the same time, all the inter-ference affecting the local sub-block, including both multiple access and inter-symbol interference, is properly modeled as the state vector of a linear system, and dynamically tracked by Kalman filtering. Two types of Kalman filters are designed, both of which are able to track an finite impulse response (FIR) MIMO channel of any memory length. The overall algorithms enjoy low complexity that is only polynomial in the number of information-bearing bits to be detected, regardless of the data block size. Furthermore, we introduce two optional performance-enhancing techniques: cross- layer automatic repeat request (ARQ) for uncoded systems and code-aided method for coded systems. We take Kalman-PDA as an example, and show via simulations that both techniques can render error performance that is better than Kalman-PDA alone and competitive to sphere decoding. At last, we consider the case that channel state information (CSI) is not perfectly known to the receiver, and present an iterative channel estimation algorithm. Simulations show that the performance of SDE with channel estimation approaches that of SDE with perfect CSI.

Characterizing a single photon detector

I will present my work about constructing and characterizing a single photon detector. Using the 1550nm laser and second harmonic light generation, I am able to count single photons on a Multi‐Pixel Photon Counter (MPPC) silicon APD. My results show that upwards of 22% quantum efficiency is achievable with the MPPC. Future work will include coincidence detection of correlated photon‐pair.

THE HOLOGRAPHIC DETECTOR FOR CLOUDS FROM TRL 5 TO TRL 8

Clouds are one of the most influential elements of weather on the earth system, yet they are also one of the least understood. Understanding their composition and behavior at small scales is critical to understanding and predicting larger scale feedbacks. Currently, the best method to study clouds on the microscale is through airborne in situ measurements using optical instruments capable of resolving clouds on the individual particle level. However, current instruments are unable to sufficiently resolve the scales important to cloud evolution and behavior. The Holodec is a new generation of optical cloud instrument which uses digital inline holography to overcome many of the limitations of conventional instruments. However, its performance and reliability was limited due to several deficiencies in its original design. These deficiencies were addressed and corrected to advance the instrument from the prototype stage to an operational instrument. In addition, the processing software used to reconstruct and analyze digitally recorded holograms was improved upon to increase robustness and ease of use.