Techniques for guided placement of a wireless device are described herein. In an example embodiment, a wi-fi wireless device comprises a radio frequency (RF) transceiver coupled to a baseband processor. The RF transceiver is configured to receive an RF signal transmitted over a wireless channel and to convert the RF signal to a modulated digital signal. The baseband processor is configured to receive the modulated digital signal from the RF transceiver, extract a wireless packet from the modulated digital signal, and compute an Exponential Effective SNR Mapping (EESM) value based on the preamble of the wireless packet, where the computed EESM value indicates the quality of the wireless channel at the current location of the wireless device. The baseband processor is further configured to provide a quality indicator based on the EESM value for the current location of the wireless device.

An abnormality detection device includes: a receiver, a reception predictor, frame information storage, and an abnormality determiner. The receiver receives a communication frame via a communication network. The frame information storage stores information regarding the communication frame. The reception predictor calculates and sets a predicted time range including a scheduled reception time of the communication frame of a target frame type from among a plurality of frame types received by the receiver by referencing the frame information storage and the reception time of the communication frame when the communication frame is received. The abnormality determiner determines the target communication frame is an abnormal frame when the target communication frame is received at a time outside the predicted reception range.

A receiver device is configured to receive a packet. The receiver device includes a physical layer circuit. The physical layer circuit is configured to demodulate the packet to acquire at least one indicator associated with the packet, and determine whether the packet is an abnormal packet or not according to the at least one indicator. If the packet is the abnormal packet, the physical layer circuit drops the packet.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for improving communication throughput despite periodic blockages. In some implementations, a method includes receiving, by a receiver and from a transmitter, code blocks transmitted according to a first set of communication parameters that includes one or more first interleaver parameters used to interleave information in the code blocks prior to transmission. Corrupted portions of at least some of the received code blocks are identified. A blockage duration and a blockage interval of a blockage of communication channel between the transmitter and the receiver are determined based on the corrupted portions of the received code blocks. A second set of communication parameters that includes one or more second interleaver parameters are determined based on the blockage duration and blockage interval. The second set of communication parameters are communicated to the transmitter for subsequent transmissions by the transmitter to the receiver.

AI-based fault detection, localization, and correction can improve message reliability in 5G and 6G communications by enabling the rapid recovery of faulted messages without wasting precious time and power on an unnecessary retransmission. The waveform of a received message is rich with information implicating the faulted message elements and, in many cases, suggesting the corrected value. In examples, message recovery can be based on the amplitude of the received waveform, its phase, any pathological variations in noise or in frequency or in polarization, and on inter-symbol transition regions, to list just a few waveform fault indicators revealing the fault locations. In addition, the AI model, or an algorithm derived from it, can discern the intent or meaning of a message, as well as its form and format, the bitwise content, the sequence of characters, and other error flags indicating which parts of the message are faulted.

A transceiver and associated method an impulse noise monitoring (INM) tool configured to identify a data stream that includes i) data transmission units (DTUs) communicating data symbols, ii) non-data symbols, and iii) non-transmission time. The INM tool is configured to ascertain an impulse noise (IN) event of the incoming data stream by evaluating a count, a frequency, a pattern, a group, or a sequence of corrupted DTUs and selected non-data symbols or non-transmission time that are proximate in time to the corrupted DTUs.

A method for transmitting feedback information, a terminal device, and a network device are provided. The method includes: a terminal device determines a target feedback mode used for transmitting feedback information, wherein the feedback information is feedback information for a transport block (TB) sent by a network device and received by the terminal device; and the terminal device uses the target feedback mode to transmit the feedback information.

Provided are a data processing method, device and node. The method includes: a second node determines that a first data sent from a first node exhibits data erasure; the second node sends feedback information to the first node, where the feedback information includes one of the following: an erasure rate, an erasure rate and an erasure position, the erasure rate is a proportion of resources that exhibit data erasure in resources corresponding to the first data, and the erasure position is a position of the resources that exhibit data erasure in the resources corresponding to the first data; the second node receiving a second data as determined according to the feedback information by the first node; the second node performs decoding according to the first item of data and the second data.

In one embodiment an apparatus, method, and system is described, the embodiment an apparatus, method including receiving a stream of data frames at an input interface, the data frames one of including security frames, or being included in security frames, wherein the security frames include payload data, performing forward error correction on the data frames a forward error correction (FEC) decoder, buffering received data frames at a buffer and blanker engine and building a complete security frame of the received data frames, determining whether or not to suppress taking a consequent action based on a frequency of authentication errors at an authentication engine, wherein the consequent action to be taken or suppressed, when taken, is taken upon payload data of one or more security frames including a data frame upon which an authentication error occurred. Related apparatus, methods and systems are also described.

A method and system for selecting a symbol detector are herein provided. A method includes extracting a first set of features for a k-th resource element (RE), where k is an integer greater than one, extracting a second set of features from a first RE to a (k1)th RE, and selecting a symbol detector for the k-th RE using a reinforcement learning (RL) neural network based on the extracted first set of features and the extracted second set of features.