Search

The Beyond fifth Generation (B5G) communication systems imposed several challenges on radio designers. For example, a machine is required to set up a call at a low Signal-to-Noise Ratio (SNR), as low as −10 dB, in the extended coverage mode. Moreover, only one receive antenna will be available, and virtually no frequency diversity. Such requirements present major challenges to maintaining timing and frequency synchronization. Carrier Frequency Offset (CFO) estimation is at the heart of these challenges. Different approaches have been proposed for CFO estimation such as maximum likelihood based on a cyclic prefix. Nevertheless, these methods remain limited in various ways. At the same time, Machine Learning (ML) techniques showed outstanding performance in several wireless communication problems. In this work, we propose an ML-based approach for CFO estimation in OFDM systems. Specifically, we propose a Gradient-Boosting Machine (GBM)-based solution to predict the CFO given the received Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). Furthermore, we make our dataset available for public access to encourage other researchers to pursue this promising direction. We compare our results with different baseline models (i.e., artificial neural networks and support vector machines). The experimental results show that our model outperforms other baseline models due to its ensemble nature which enables ensemble models to obtain a better generalization behavior.

Over the past several years, sponsored mobile data and the payment directions on the Internet have been two major subjects in network economics. Several tier-1 service providers (SPs) created their frameworks for sponsored mobile data by cooperating with content providers. Based on these frameworks, users can have free data transfer if they accomplish a predefined task such as watching advertised videos. In this paper, we investigate particular types of mobile applications that can deliver their data to all cellular users free, even to the users without a data plan. Our approach does not force users to click on advertised content to obtain free data access yet it can still generate a level of revenue for application providers (APs) that can compensate for the revenue loss of the network service provider. We call this approach an Application-Oriented Free Data (AFD) program. To model and analyze the characteristics of the considered framework we use a multi-stage game consisting of cellular users, an SP, and an AP. We solve this game by backward induction. In this way, we define the required thresholds of price and data usage for an AFD program. The feasibility of the AFD program is illustrated by several numerical examples.