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The speed estimation has been widely used for tracking mobile device locations, providing essential information in location/mobility-aware communications, enhancing received signal quality/robustness, and reducing energy consumption and latency. Deep learning can be used to improve the performance constrained by signal/system model. This work focuses on the issues on machine learning (ML) based speed estimation using primary synchronous signal (PSS), which is embedded in the 5G standards, over general time-variant multipath channels. Aiming to reduce the complexity involved in the ML algorithms for the speed estimation in mobile networks, we propose a pipelined ML algorithm to decompose the original ML model into several smaller ones. The advantages of the proposed convolutional neural network (CNN) based approach have been verified by simulations.

This article investigates several critical issues on the ergodic sum capacity and system outage performance of downlink non-orthogonal multiple access (NOMA) systems with different numbers of users per cluster sharing the same resource. The outcomes from this work suggest that NOMA systems with more users per cluster perform better under a low channel gain, while those with fewer users per cluster perform better under a high channel gain. It discloses a fact that a traditional NOMA system with a fixed user grouping scheme may not be a good choice to deal with complex channel conditions with diverse applications. To address the issue, this article introduces a concept of mixed user grouping NOMA, which outperforms OMA and traditional NOMA significantly. The work in this article lays a foundation for more further investigations on mixed user grouping NOMA systems.