Abstract: According to an aspect, a mobile device capable of communicating with a base station using a communication mode from plurality of communication modes comprising, a set of sensors configured to provide a data representing a terrain condition of the mobile device, a distributed machine learning engine determining a first terrain state at regular interval based on a current data and a past data received from the set of sensors, and an inference unit determining a second terrain state of the mobile device from the current data and the first terrain state, wherein, the base station allocating the communication mode from plurality of communication modes based on the second terrain state.

Abstract: The detection and validation of Secondary Synchronization Signal comprising generating a set of samples by performing DFT operation on a time domain LTE signal, wherein the signal comprising an LTE frame divided into an even half and odd half frame, First and second set of hypotheses from even samples in even and odd half frame are generated and third and fourth set of hypotheses from odd samples in even and odd half frame are generated using first and second hypotheses. Even half frame is selected as start of boundary of the frame when location of the peak of first hypotheses is smaller than that of second hypotheses or location of the peak of fourth hypotheses is smaller than that of third hypotheses. The physical layer cell identity is determined from the locations of the peak of the first, second, third and fourth set of hypotheses averaged over multiple frames.

Abstract: The input samples transmitted by the base station are decimated and the decimated samples are correlated with the three reference PSS sequences to obtain the correlation results. The correlation results are differentially combined to obtain the hypotheses. The hypotheses are grouped into windows. The peaks across windows of multiple half frames are selected and obtained peaks are averaged over the multiple half frames to detect the half frame boundary window. Reference PSS versions are generated for ICFO estimation. The hypotheses are generated in the HFBW. The highest averaged hypothesis is selected to estimate ICFO. The ICFO error is corrected by adjusting a reference clock signal. The decimated samples are correlated and differentially combined around the HFB to determine a HFB and a FCFO from the peak value of the hypotheses. The received samples are correlated and differentially combined around the half frame boundary to further refine HFB and FCFO estimations.