A detector in a mobile device receives input from a modem, determines whether the mobile device is indoor or outdoor based on the modem-supplied input, and stores in memory a binary value to indicate an indoor-outdoor state. In some embodiments, the detector extracts a feature from the modem-supplied input, and uses the extracted feature with a statistical classifier, to output an indoor-outdoor state and an associated probability of correctness of the indoor-outdoor state. In other embodiments, the detector determines whether the mobile device is indoor or outdoor based at least on the feature extracted to characterize temporal distribution of the wireless signal, by using a prior value of the state being indoor or outdoor.

Embodiments of this disclosure provide an apparatus and method for estimating a frequency offset, an apparatus and method for estimating a channel spacing and a system. The apparatus for estimating a frequency offset includes: a synchronization extracting unit configured to perform a training sequence synchronization extraction on a receiving sequence containing a periodic training sequence to obtain the training sequence; a delay correlation processing unit configured to parallelly perform autocorrelation operations of different delay amounts on the training sequence to obtain multiple parallel correlation sequences; a superimposition processing unit configured to perform a superimposition operation on the multiple parallel correlation sequences to obtain a single sequence; and a frequency offset estimating unit configured to determine a frequency offset according to a phase of a synchronization position of the single sequence in the training sequence. With the embodiments of this disclosure, anti-noise characteristic of the frequency offset estimation may be improved.

Embodiments herein relate to a method in a user equipment for performing channel estimation of one or more long term channel properties at the user equipment, wherein the user equipment is served by a radio network node.

The present disclosure relates to a method and related mobile device for estimating a channel by choosing a subset of correlated channel coefficients and estimating the reference signal received power of the subset of correlated channel coefficients based on a cross-correlation between channel coefficients of the subset of correlated channel coefficients.

Disclosed is a pilot allocation method based on coherence time for a large-scale multiple input multiple-output (MIMO) system. The present invention achieves optimal allocation of pilot resources by fully utilizing the feature that different users possibly have different moving speeds and coherence time of corresponding channels is accordingly different, thereby improving overall data transmission performance of the system and achieving certain practicability. Moreover, the present invention effectively uses limited transmission resources in the case of limited total transmission resources, thereby improving overall data transmission performance of the system and effectively reducing pilot contamination.

According to one or more embodiments of the disclosure, a device receives data regarding wireless communications between a wireless access point and a client. The device evaluates, based on the data, motion of the client relative to the wireless access point. The device makes, based on the motion of the client relative to the wireless access point, a determination that the motion of the client relative to the wireless access point will result in the wireless communications degrading as the client approaches the wireless access point. The device adjusts the wireless communications, based on the determination that the motion of the client relative to the wireless access point will result in the wireless communications degrading as the client approaches the wireless access point.

Embodiments include apparatuses and methods of identifying line of sight, LOS, and non-line of sight, NLOS, conditions in a multipath channel of a vehicular communication system. It is proposed to equip the receiving node with a dual antenna receiver. Then the proposed solution uses the first cluster of multipath components of channel estimates measured on the two antennas to derive the LOS/NLOS channel conditions based on hypothesis testing.

Methods are proposed to derive measurement results for User Equipment (UE) antenna selection, beam selection, cell selection, handover, and radio resource management (RRM). First, UE determines its mobility state by using at least two of the following metrics: 1) Doppler information (e.g., from mobility detection gear, MD); 2) beam ping-pong rate, beam change rate, beam change per time period; and 3) moving speed and moving direction from an accelerometer sensor, rotation speed from a gyroscope, ambient magnetic field from a magnetic field sensor, and at least one active antenna set. Next, UE uses an averaging number that is adapted based on its mobility state to derive an average measurement result including at least one of RSRP, RSRQ, RSSI, IL, SNR, and SINR. Finally, UE performs antenna selection, beam selection, cell selection, or RRM based on the average measurement result and joint consideration.

An apparatus for estimating a DOA in a MIMO system includes a receiver and a signal processor. The receiver receives Rx signals from a target through Rx antennas after Tx signals having different phases are transmitted through Tx antennas, and transforms the Rx signals into time domain Rx signals. The processor transforms the time domain Rx signals into Rx signals in a frequency domain including a range-related domain and a velocity-related doppler domain; divides the doppler domain into regions according to a phase difference between the Tx signals; extracts signals from the regions; combines the signals to form first and second arrays; determines a minimum value for each of the first and second arrays using a DML algorithm; selects one of the first and second arrays having the minimum value as a true array; and estimates a DOA corresponding to the true array as an actual DOA of the target.

A method performed by a communication device includes generating an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device. The method also includes inferring, with a neural network, a residual of the initial channel estimate of the current time step. The method further includes updating the initial channel estimate of the current time step based on the residual.