Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
This work addresses detection challenges in 5G-NOMA networks, offering potential improvements in spectrum efficiency and connectivity, but it is incremental as it builds on existing SIC and MMSE methods.
The paper tackles the trade-off between cluster size and error in 5G-NOMA uplink detection by proposing an online adaptive machine learning approach, demonstrating through simulations that it outperforms MMSE-SIC based detection for large cluster sizes.
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.