Embodiments of this application provide slot format configuration method and device. According to the method, a network device determines a slot format index for performing uplink-downlink transmission with a terminal device. The slot format index corresponds to a row in a slot format information table, and each row in the slot format information table includes positions of uplink symbols, positions of downlink symbols, and positions of unknown symbols in a slot. The network device sends the slot format index to the terminal device. The terminal device determines a slot format for the uplink-downlink transmission from the slot format information table based on the slot format index. The uplink-downlink slot configuration method provided in the embodiments of this application may satisfy specific requirements of low-latency and high-reliability communication scenarios.

The embodiment of the present disclosure provides a random access failure processing method, which includes: determining a cell and a carrier used to send a preamble for random access when the preamble is sent to a base station a number of times greater than a preset number; and transmitting, under a case where the cell used to send the preamble is a primary cell configured with an SUL carrier and a non-SUL carrier, a message indicating presence of problems in the random access to the base station through the SUL carrier if the carrier used to send the preamble is the non-SUL carrier. According to the embodiment of the present disclosure, if it is determined that the non-SUL carrier of the primary cell has a problem, the SUL carrier may not have a problem even if the SUL carrier has a problem because the primary cell is further configured with the SUL carrier and the SUL carrier generally has better performance than the non-SUL carrier. Therefore, the message indicating presence of problems in the random access can be transmitted to the base station through the SUL carrier, so as to ensure that the base station can timely receive the problems and thereby punctually deal with the problems.

Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

Systems and methods providing for dynamic switching between the various waveforms on the downlink are described. Embodiments of a dynamic downlink waveform switching implementation may, for example, support utilization of one or more multiple carrier (MC) waveform (e.g., OFDMA) or other high peak to average power ratio (PAPR) waveform and one or more SC (SC) waveform (e.g., discrete Fourier transform-spread-orthogonal frequency division multiplexing (DFT-S-OFDM)) or other low PAPR waveform. Dynamic selection of a downlink waveform may be made by an access point based upon various metrics, including relative distance to a served an access terminal and the preference of downlink waveform indicated by a served an access terminal. A downlink waveform selection indication may be signaled from the access point to the served an access terminal using downlink control information (DCI).

Systems, devices, and methods of the present invention enhance data transmission through the use of polynomial symbol waveforms (PSW) and sets of PSWs corresponding to a symbol alphabet is here termed a PSW alphabet. Methods introduced here are based on modifying polynomial alphabet by changing the polynomial coefficients or roots of PSWs and/or shaping of the polynomial alphabet, such as by polynomial convolution, to produce a designed PSW alphabet including waveforms with improved characteristics for data transmission. In various embodiments, transmitter and receivers utilize symbol waveforms based on a PSW alphabet designed using methods that may include specifying the location of particular polynomial roots, such as placing roots at the symbol time boundaries with amplitude zero, to minimize symbol boundary discontinuities, translating the nearest polynomial root to a particular point in the complex plane, directly editing the location of one or more polynomial roots, adjusting the complex conjugate of an edited PSW root in order to keep the PSW real-valued, and shaping one of more PSWs using polynomial convolution with polynomial versions of the raised cosine, root raised cosine, Gaussian, or other band-limiting filters. Stochastic methods for PSW optimization are also introduced.

The present invention relates to a wavelet bandpass sampling method, with low aliasing and a corresponding device. The analogue signal to sample is correlated with a sequence of wavelets succeeding each other with a rate f.sub.p of which the positions in the sequence are temporally modulated from arguments of a CAZAC sequence, notably a Zadoff-Chu sequence. The correlation results are next sampled at a frequency f.sub.sf.sub.p and digitally converted to provide a compressed representation of the signal. The temporal modulation of the positions of the wavelets makes it possible to obtain incoherent aliasing of the correlation signal in the sampling band and thereby to reduce aliasing.

Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

Systems and methods providing for dynamic switching between the various waveforms on the downlink are described. Embodiments of a dynamic downlink waveform switching implementation may, for example, support utilization of one or more multiple carrier (MC) waveform (e.g., OFDMA) or other high peak to average power ratio (PAPR) waveform and one or more SC (SC) waveform (e.g., discrete Fourier transform-spread-orthogonal frequency division multiplexing (DFT-S-OFDM)) or other low PAPR waveform. Dynamic selection of a downlink waveform may be made by an access point based upon various metrics, including relative distance to a served an access terminal and the preference of downlink waveform indicated by a served an access terminal. A downlink waveform selection indication may be signaled from the access point to the served an access terminal using downlink control information (DCI).

Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

A relay transmission system includes a relay unit configured to relay uplink signals and downlink signals in the first and second communication systems, a time division duplex (TDD) information estimation unit configured to estimate a transmission period of network devices in the first communication system on the basis of the uplink or downlink signal of the first communication system that is relayed by the relay unit, a surplus bandwidth determination unit configured to determine a surplus bandwidth in which an uplink signal of the first communication system is not allocated to a relay target of the relay unit during the transmission period on the basis of the number of network devices and a maximum transmission capacity of the network devices, and a bandwidth allocation unit configured to allocate the uplink signal of the second communication system to the relay target of the relay unit in the surplus bandwidth.