Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach

Non-orthogonal multiple access (NOMA) has emerged as a promising radio access technique for enabling the performance enhancements promised by the fifth-generation (5G) networks in terms of connectivity, low latency, and high spectrum efficiency. In the NOMA uplink, successive interference cancellation (SIC) based detection with device clustering has been suggested. In the case of multiple receive antennas, SIC can be combined with the minimum mean-squared error (MMSE) beamforming. However, there exists a tradeoff between the NOMA cluster size and the incurred SIC error. Larger clusters lead to larger errors but they are desirable from the spectrum efficiency and connectivity point of view. We propose a novel online learning based detection for the NOMA uplink. In particular, we design an online adaptive filter in the sum space of linear and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design is robust against variations of a dynamic wireless network that can deteriorate the performance of a purely nonlinear adaptive filter. We demonstrate by simulations that the proposed method outperforms the MMSE-SIC based detection for large cluster sizes.Comment: Accepted at ICC 201

Expert Object Recognition in video

A recent computer vision technique for object classification in still images is the biologically-inspired Expert Object Recognition (EOR). This thesis adapts and extends the EOR approach for use with segmented video data. Properties of this data, such as segmentation masks and the visibility of an object over multiple frames, are exploited to decrease human supervision and increase accuracy. Several types of runtime learning are facilitated: class-level learning in which object types that are not included in the training set are given artificial classes; viewpoint-level learning in which novel views of training objects are associated with existing classes; and instance-level learning of images that are somewhat similar to training images. The architecture of EOR, consisting of feature extraction, clustering, and cluster-specific principal component analysis, is retained. However, the K-means clustering algorithm used in EOR is replaced in this system by an augmented version of Fuzzy K-means. This algorithm is incrementally run over the lifetime of the system, and automatically determines an appropriate number of partitions based on the data in memory and on a system parameter. In addition, the edge and line-based feature extraction of EOR is replaced with a global application of the principal component analysis, which increases accuracy when used with segmented video data. Classification output for the system consists of a multi-class hypothesis for each tracked object, from which a single-class hard hypothesis may be determined. The system, named VEOR (video expert object recognition), is designed for and tested with noisy, automatically segmented real-world data, consisting of both videos and still images of vehicle (car, pickup truck, and van) profiles