A user equipment (UE) determines a receive (RX) spatial filter for receiving both a first measurement resource and a second measurement resource. The RX spatial filter is determined based on a first spatial quasi-co-located (QCL) reference associated with the first measurement resource and a second spatial QCL reference associated with the second, measurement resource. The UE measures the first and second measurement resources with the determined Rx filter configuration.

Artificial Intelligence (AI) can rapidly evaluate a faulted message in 5G or 6G, calculate a likelihood that each message element is faulted, and optionally suggest a most probable corrected version for each of the likely faulted message elements. To do so, the AI takes in numerous factors besides the message itself, such as the modulation quality of each message element, the proximity and quality of a nearest demodulation reference, a signal-to-noise ratio of the message element, a measure of current electromagnetic noise during the message element, an expected format or expected codewords based on prior messages or convention, and other factors. The AI model can then provide guidance as to mitigation, such as choosing whether to request a retransmission or attempting to vary the likely faulted message elements. The AI model can be adapted to fixed-site computers or to the more limited computers of a mobile user device.

A receiver can determine that a received message is corrupt according to an associated error-detection code in 5G/6G. The receiver can then calculate the modulation quality of each message element of the message by comparing a modulation value of the message element with a set of predetermined modulation levels, wherein a larger deviation from the closest predetermined modulation level corresponds to a lower modulation quality. Noise and interference usually generate random changes in the modulation as-received, and this generally results in a larger deviation relative to the predetermined modulation levels and hence a lower modulation quality. The receiver can count the number of faulted message elements with modulation quality below a threshold, and if the number of faults is less than some limit, the receiver can attempt to recover the message by altering the worst-quality message elements, testing each version for consistency.

With increasingly dense wireless traffic in 5G and 6G networks, the incidence of message faults due to interference is increasing, leading to wasted time and energy on multiple re-transmissions. Disclosed are procedures for assembling a fault-free copy of a message from two corrupted copies. First, measure the modulation quality of each message element. A faulted message element usually has poor modulation quality. Then, select the best message elements from each of the two corrupted copies, and test the merged version against an embedded error-detection code. If the merged copy still fails the test, select each of the message elements that are different in the two faulted copies since they are all suspicious, and test each version with the error-detection code. By recovering a message despite reception errors, another transmission is avoided, saving time and energy, and avoiding contributing yet further to the background noise. Many additional aspects are disclosed.

Prior art includes error detection according to an embedded CRC (cyclic redundancy code) or the like, and error correction using FEC (forward error correction) codes, but achieves only partial success in practice, leading to frequent requests for message retransmission. Disclosed is a method for detecting errors in individual message elements using 5G or 6G technologies, by measuring the modulation quality according to how far the amplitude or phase of the message element deviates from the calibrated modulation levels of the modulation scheme. A large deviation indicates a faulted message element, whereas a close match with the calibrated modulation levels is likely correct. By identifying faulted message elements individually, the receiver can recover the message using a number of strategies, disclosed herein. With improved error detection, and localization to individual message elements, network communications can be substantially upgraded at negligible cost, according to some embodiments.

Wireless receivers in 5G and 6G are generally configured to discard faulted messages and request retransmission of the entire message. Disclosed is a procedure enabling the receiver to determine which specific message elements are faulted, and then to request only the faulted portion be retransmitted. Substantial time and wasted power can thereby be saved. The receiver can identify the faulted message element(s) by calculating a modulation quality of each message element and specifying only that portion of the message containing those message elements. For example, the receiver can determine the modulation quality by comparing a difference between the message element's amplitude or phase modulation and the closest predetermined amplitude or phase level of the modulation scheme. A deviation larger than a threshold value strongly suggests that the message element is wrong. The SNR and other factors can also be included in a formula to identify faulted message elements.

Upon receiving a corrupted message in 5G or 6G, a receiver generally rejects the message or ignores it entirely, because determining which message elements are faulted is difficult and complex. AI-based procedures are provided for localizing faults in specific message elements, and for determining the corrected values when possible. AI inputs may include the amplitude or phase modulation quality of each message element, the measured SNR of each message element, the modulation quality of a preceding demodulation reference, and current backgrounds, among other factors. After training (adjusting according to measured network data), the AI model may then determine the most likely faulted message elements, and may also direct the search for the most likely corrected values. By recovering the original corrected message without an unnecessary retransmission, the system can save time, reduce transmission energy, and avoid generating backgrounds. Many additional aspects are disclosed.

Techniques are provided for positioning of a mobile device in a wireless network using directional positioning reference signals (PRS), also referred to as PRS beamforming. In an example method, a plurality of directional PRSs are generated for at least one cell for a base station, such that each of the plurality of directional PRSs comprises at least one signal characteristic and a direction of transmission, either or both of which may be distinct or unique. The plurality of directional PRSs is transmitted within the at least one cell, such that each of the plurality of directional PRSs is transmitted in the direction of transmission. A mobile device may acquire and measure at least one of the directional PRSs which may be identified using the associated signal characteristic. The measurement may be used to assist position methods such as OTDOA and ECID and to mitigate multipath.

Techniques are provided for positioning of a mobile device in a wireless network using directional positioning reference signals (PRS), also referred to as PRS beamforming. In an example method, a plurality of directional PRSs are generated for at least one cell for a base station, such that each of the plurality of directional PRSs comprises at least one signal characteristic and a direction of transmission, either or both of which may be distinct or unique. The plurality of directional PRSs is transmitted within the at least one cell, such that each of the plurality of directional PRSs is transmitted in the direction of transmission. A mobile device may acquire and measure at least one of the directional PRSs which may be identified using the associated signal characteristic. The measurement may be used to assist position methods such as OTDOA and ECID and to mitigate multipath.

Upon receiving a corrupted message in 5G or 6G, a receiver generally rejects the message or ignores it entirely, because determining which message elements are faulted is difficult and complex. AI-based procedures are provided for localizing faults in specific message elements, and for determining the corrected values when possible. AI inputs may include the amplitude or phase modulation quality of each message element, the measured SNR of each message element, the modulation quality of a preceding demodulation reference, and current backgrounds, among other factors. After training (adjusting according to measured network data), the AI model may then determine the most likely faulted message elements, and may also direct the search for the most likely corrected values. By recovering the original corrected message without an unnecessary retransmission, the system can save time, reduce transmission energy, and avoid generating backgrounds. Many additional aspects are disclosed.