438 results about "Multi output" patented technology

Described are concepts, systems, system architectures, circuits, methods, and techniques directed toward power management and control. In particular, described are concepts, systems, system architectures, circuits, methods, and techniques for implementing power converters that may not have a right-hand pole zero in their linearized, averaged control-to-output transfer function, but that may still have buck and boost functionality.

A DC-DC conversion apparatus for a charging pile includes a group of power terminals, a plurality of groups of load terminals, and a plurality of isolated DC-DC conversion circuits. An input end of each isolated DC-DC conversion circuit is connected to the group of power terminals, and output ends of the plurality of isolated DC-DC conversion circuits are connected to the plurality of groups of load terminals in a one-to-one correspondence, so that the DC-DC conversion apparatus for a charging pile forms an architecture with a single input and a plurality of independent outputs. In this way, the DC-DC for a charging pile can have high power utilization while implementing wide-range power outputs.

Apparatuses, systems, and techniques to detect uplink data in a multi-user multiple input multiple output wireless communication system. In at least one embodiment, uplink data is detected using one or more graphics processing units to perform parallel computations in order to form a consensus belief about information received by one or more base stations in a wireless network.

Technologies for providing energy efficiency technology in extreme mMIMO systems in a cellular network cellular network (e.g., 5G wireless network, 6G wireless network) are described. The method collects data representing conditions for potential energy saving (ES) modes, comprising morphology data and traffic pattern data. The method determines, using the collected data, one or more energy saving (ES) modes for one or more components of a cellular network in at least one of a time (T) domain, a frequency (F) domain, or a space (S) domain, wherein the one or more ES modes cause at least one adjustment to the one or more components in the at least one of the time (T) domain, the frequency (F) domain, or the space (S) domain.

Method and apparatus for supporting simultaneous MultiUser (MU) Multiple-Input Multiple-Output (MIMO) operations utilizing Orthogonal Frequency Division Multiple Access (OFDMA) with Uplink OFDMA Random Access (UORA). Access Points (APs) trigger and obtain short PPDUs from the stations to learn spatial filtering patterns that are utilized in adjusting antenna phase differences to obtain minimum / maximum energy from this BSR. The APs determine favored and unfavored directions of communication, which are conveyed to non-AP UORA stations as channel estimation signals. The UORA stations base broadcasting on strengths of channel estimation signals to determine eligible UORA resource units. The UORA procedure to secondary APs allows a primary AP to trigger a UL-MU MIMO transmission with the number of spatial streams exceeding the number of antennas at the primary AP.

A system and method for enabling downlink signal transmission in a Multiple-Input Multiple-Output (MU-MIMO) system is provided. The method comprises of creating a scheduling information based on MU-MIMO Scheduling of a plurality of User equipments (UES), multiplexing the plurality of UES paired based on the scheduling decisions across time, frequency and code, inputting transport blocks of each UE pushed on to a Downlink shared channel (DLSCH), performing bit processing of each of the CRC encoded UEs, inputting Code-rate (MCS) for data modulation mapping on a post bit processed data, assigning DM-RS ports to the UE based on allocated MCS and number of antenna ports from a scheduling information structure, adding, a Partial Port Occupancy information (P-Poi) for each UE along with the scheduler information structure required for demodulation at UE and enabling optimal channel estimation at UE by adding new DM-RS port combinations using reserved fields in the DCI messaging.

For example, an apparatus may include a baseband controller configured to control a plurality of dual-polarization Radio Heads (RHs) to communicate a Multiple-Input-Multiple-Output (MIMO) transmission, the baseband controller configured to control a first dual-polarization RH of the plurality of dual-polarization RHs to communicate a first spatial stream of the MIMO transmission with a horizontal-polarization via one or more first dual-polarization antenna elements of the first dual-polarization RH, and to control a second dual-polarization RH of the plurality of dual-polarization RHs to communicate a second spatial stream of the MIMO transmission with a vertical-polarization via one or more second dual-polarization antenna elements of the second dual-polarization RH.

The invention relates to the technical field of radars, and provides a frequency diversity array multi-input multi-output radar target detection system. The system comprises a detection statistic construction module, a detection optimization module, a detection threshold determination module and a target judgment module. Wherein the detection statistic construction module is used for constructing a detection statistic; according to the detection statistics, constructing a semi-definite programming optimization model; the detection optimization module is used for constructing a semi-definite programming optimization model according to the detection statistics; the detection optimization module is also used for solving the optimization model to obtain an optimal value of the detection statistics; and the target judgment module is used for comparing the detection statistic optimal value with a detection threshold so as to detect whether a target exists or not, so that the target detection probability and the anti-interference capability of the frequency diversity array multi-input multi-output radar in a complex environment are improved.

The invention provides a multiple-input-multiple-output soft measurement method and device and a medium. The method comprises the steps that historical multi-sampling-rate process variable data and historical multi-quality variable data are acquired; obtaining a plurality of historical task specific features from the historical multi-sampling-rate process variable data; inputting the plurality of historical task specific features to obtain historical cross-task sharing interaction features; training according to the historical cross-task sharing interaction features, the plurality of historical task specific features and historical multi-quality variable data to obtain a trained feature interaction hybrid expert network model; and obtaining a plurality of online prediction results according to the trained feature interaction hybrid expert network model. According to the method, the device and the medium, the problems that information loss, time alignment errors and task negative migration are easily caused when different variable sampling frequencies are inconsistent in an existing multi-input multi-output soft measurement method, an irregular sampling phenomenon exists, and the prediction precision of multiple quality indexes is not high can be solved.

A method for performing multiple-input and multiple-output channel estimation includes: generating, using a first machine-learning model, an initial set of estimated channel information from a first input set of channel information, wherein the first input set of channel information corresponds to a first plurality of radio-frequency chains, and wherein the estimated set of channel information corresponds to the first plurality of radio-frequency chains and a second plurality of radio-frequency chains; generating, using a second machine-learning model, a set of estimated channel phases from the initial set of estimated channel information and a second input set of channel information, wherein the second set of input channel information corresponds to the second plurality of radio-frequency chains; and combining the initial set of estimated channel information and the set of estimated channel phases to generate an enhanced set of estimated channel information.

An embodiment provides for a base station (BS) in a wireless network, the BS including memory and a processor coupled to the memory. The processor can cause the BS to determine a first gain region of a first beam index associated with serving a first user equipment (UE) and determine a first interference region of the first beam index associated with serving a second user equipment (UE). The processor can further calculate a beamforming weight of the first beam index using an analog null algorithm wherein the first gain region and the first interference region are inputs to the analog null algorithm to optimize for a threshold criterion. The processor can also transmit a first beam having the beamforming weight of the first beam index as part of a multi-user multiple-input multiple-output (MU-MIMO) operation.

A high gain multiple input, multiple output antenna which has a feeding board; a ground plane; a low band patch radiator; a first printed circuit board; a second printed circuit board; and a high band dipole patch radiator. The high band dipole patch radiator is a co-located stacked array on top of the low band patch radiator. The high band dipole patch radiator and the low band patch radiator are proximity coupled with the first printed circuit board and the second printed circuit board. The low band patch radiator covers low band frequencies in the range of 2.4 GHz to 2.5 GHZ and the high band dipole patch radiator cover high band frequencies in the range of 4.9 GHZ to 7.125 GHz. The antenna assembly has a gain of above 7.5 dBi and a well-directed radiation patten with no side lobes.

A method and system are disclosed for optimizing downlink multi-user multiple-input multiple-output (MU-MIMO) transmission in time division duplex (TDD) wireless communication systems. Sounding reference signals (SRS) from user equipment (UEs) are processed using a machine-learned model to extract physical-layer channel characteristics, including Doppler spread, delay profile, and signal-to-noise ratio (SNR). Based on these features, a channel state information (CSI) acquisition operation is adaptively selected for each UE—either channel estimation (CE) alone or both CE and channel prediction (CP). When CP is used, prior channel estimates are analyzed to generate predicted CSI over a future interval. The obtained CSI is then used to configure MU-MIMO transmission parameters such as beamforming weights, modulation and coding schemes (MCS), and UE grouping. This adaptive framework improves performance in time-varying and high-mobility environments by ensuring timely and reliable CSI is used for downstream transmission decisions.

Techniques and apparatus for radio frequency (RF) exposure compliance for multiple-input, multiple-output (MIMO) transmissions are described. An example method that may be performed by a wireless device includes determining a respective multiple-input, multiple-output (MIMO) transmit power limit for each of a plurality of antennas of the wireless device. A signal associated with a MIMO transmission is transmitted from each antenna at a respective first transmission power level based on the respective MIMO transmit power limit for the antenna in compliance with a radio frequency (RF) exposure limit. The first transmission power level used for a first antenna of the plurality of antennas is different from the first transmission power level used for a second antenna of the plurality of antennas.

The invention provides a multi-energy harvesting and multi-output management converter, and relates to the field of integrated circuits. An energy collection control circuit of a multi-energy collection and multi-output management converter is composed of a plurality of circuits. The input end maximum power tracking circuit is used for collecting input voltage and energy storage capacitor voltage and generating a first control signal. And the output end energy demand monitoring circuit is used for collecting the load voltage to obtain a second control signal. And the mode selection circuit is used for generating an enable signal according to the first control signal and the second control signal. And the on-time self-adjusting circuit is used for generating a third control signal according to the energy input voltage and the enable signal so as to control the on-time. And the adaptive zero current detection circuit is used for acquiring a fourth control signal of zero current conversion time based on the energy input voltage and the enable signal. And the grid control and drive circuit is used for outputting a fifth control signal according to the third control signal and the fourth control signal, and the converter can improve the energy collection and conversion efficiency.

A multiple-input and multiple-output (MIMO) radar system, including a horizontal antenna array having horizontal elements to detect an azimuth angle estimation, the horizontal elements being arranged in a sparse and non-sparse distribution, a vertical antenna array having vertical elements to detect an elevation angle estimation, the vertical elements being arranged in a sparse and non-sparse distribution, and a two-dimensional antenna array including a portion of the horizontal antenna array and a portion of the vertical antenna array. The system is configured to estimate, using the horizontal antenna array, an azimuth angle, to estimate, using the vertical antenna array, an elevation angle, to identify, based on the azimuth angle and the elevation angle, one or more ambiguities, and to analyze, using a portion of the two-dimensional antenna array, the one or more ambiguities to determine a more accurate azimuth angle and elevation angle.

The invention relates to the technical field of automatic control, in particular to a high-order iterative learning control method for a nonlinear non-repetitive system, which comprises the following steps of: 1, establishing a system model: aiming at the first iteration, establishing a discrete time multiple-input multiple-output system model with multi-source non-repetitive uncertainty; 2, defining and correcting a tracking error: introducing a random variable obeying Bernoulli distribution, and correcting the tracking error to process a track length of iterative change; 3, designing a high-order iterative learning control law: updating a current control signal by adopting control input and correction tracking errors based on a plurality of previous iteration periods; and 4, applying the high-order iterative learning control law to the controlled system, and updating the control input of the next iteration by using the corrected tracking error obtained by each iteration, so that the control output of the system tracks an expected trajectory under the meaning of mathematical expectation, and the expected value of the tracking error is finally converged to a bounded region.

The invention discloses an image rain removal model, method and system, electronic equipment and a storage medium, and the image rain removal model employs a multi-output multi-scale architecture and has a plurality of scale branches operated under a plurality of resolutions; the encoder of each scale branch comprises a convolution layer which is used for receiving and extracting potential features of a to-be-processed rain image and obtaining a shallow feature map; the frequency feature enhancement unit is used for capturing multi-frequency information, acquiring context semantic information and acquiring a processing feature map; the multi-scale compensation Transform block is used for extracting global information and local detail features in each scale branch to obtain a deep feature map; a gating fusion module is arranged at the tail end of the encoder and is used for fusing depth features of all scale branches to obtain an enhanced feature map; a residual convolutional layer for outputting a residual image is arranged at the tail end of the decoder of each scale branch; the residual image is used for subtracting the received to-be-processed rain image to obtain a rain-removed image.

The invention provides a multi-input and multi-output frequency coupling admittance modeling method for a follow-up network fan, and belongs to the technical field of large-scale follow-up network type and follow-up network type fan frequency coupling admittance modeling. The technical problem that a network following type GFL and network constructing type GFM doubly-fed fan series-parallel system in a wind power plant lacks an accurate small-signal modeling and analysis method is solved. According to the technical scheme, the method comprises the following steps: S1, establishing an open-loop doubly-fed motor self admittance considering stator and rotor flux linkage dynamics; s2, obtaining frequency domain multiple-input multiple-output admittances of the following network type doubly-fed fan and the constructed network type doubly-fed fan; s3, obtaining a unified multiple-input multiple-output admittance frequency domain model containing a closed loop interconnection system formed by the following network type doubly-fed fan admittance YGFL and the construction network type doubly-fed fan admittance YGFM; and S4, quantitatively evaluating a phase-locked loop parameter and a virtual synchronous control parameter. According to the invention, accurate determination of frequency coupling oscillation is realized.

The invention discloses a multi-input multi-output routing scheduling device, which comprises an input port module used for acquiring transaction data in real time, analyzing the transaction data to obtain a destination address, and caching the transaction data to a corresponding virtual output queue according to the destination address of the transaction data, when the transaction data volume cached in any virtual output queue reaches a preset data sending threshold value, a transmission request is sent to an output port module matched with the virtual output queue; the output port module is used for arbitrating at least one transmission request sent by each input port module in a set time interval to obtain a target virtual output queue located on a target input port module, and continuously reading a set number of complete data sequences from the target virtual output queue on the target input port module and sending the complete data sequences. According to the device disclosed by the invention, the utilization rate of cache resources is improved, and the condition of data congestion is effectively relieved.

The invention discloses a multi-inductor multi-output DC voltage converter and a control method thereof, the converter comprises a plurality of basic unit modules and a charge redistribution module, the plurality of basic unit modules are connected in series, and the plurality of basic unit modules are all connected with the charge redistribution module. The method comprises the steps that according to an input voltage signal, based on a basic unit module, a first inductor is sequentially and alternately magnetized and demagnetized in a complete period, and load end output current and load end output voltage are obtained; and obtaining the first flight capacitance current, combining the load end output current and the load end output voltage, performing adjustment processing on the inductance current based on a charge redistribution module, and outputting the adjusted inductance current. According to the embodiment of the invention, the capability of supporting higher input voltage and more output channels can be enhanced, and external elements are reduced, so that the conversion efficiency of the system is improved. The method can be widely applied to the integrated circuit design technology.

The invention discloses a model predictive control-based joint simulation method for a gas-supplementing and enthalpy-increasing cascade steam heat pump, and belongs to the technical field of gas-supplementing and enthalpy-increasing cascade steam heat pumps. The method comprises the following steps: S1, establishing a dynamic model of each component of the gas-supplementing enthalpy-increasing cascade steam heat pump; s2, connecting all parts according to a unified interface to form a system overall dynamic model, and performing dynamic simulation; s3, the dynamic simulation data of the air-supplementing and enthalpy-increasing cascade type steam heat pump system is applied, and multi-output and multi-input state space model identification is carried out; s4, using the identified state space model as a prediction model, and developing a model prediction controller MPC; the behavior of the system in a period of time in the future is predicted, the optimal control input is solved through an optimization method, and real-time and accurate control over the system is achieved. According to the invention, when the system deals with external load change and working condition change, the operation safety of the system is maintained while rapid and accurate load tracking and temperature control are realized.

A power modulator is provided. The power modulator includes: a multi-output voltage regulator (MOVR) configured to output a plurality of voltages in a discrete level envelope tracking mode (DLETM), the plurality of voltages having mutually different levels corresponding to reference output voltage signals; a switching regulator configured to output a switching regulator voltage, the output voltage depending on a selected voltage among the plurality of voltages and the switching regulator voltage in the DLETM, and the output voltage depending on the switching regulator voltage in the average power tracking mode; a switching regulator controller configured to sense an output current of the MOVR to obtain a sensed value, and to control the switching regulator based on the sensed value in the DLETM; a switch array including a plurality of switches corresponding to the plurality of voltages, respectively, and configured to selectively connect the selected voltage to a power amplifier by performing a switching operation; and a switch controller configured to control the switching operation.

A uniform circular array (UCA) is assisted with an analog inverse fast Fourier Fast transform (IFFT) / fast Fourier transform (FFT) precoder to reduce computational complexity in one or more line-of-sight (LoS) multiple-input multiple-output (MIMO) networks. Each port of the UCA includes a steerable miniaturized array with beam steering capability. Analog beam steering is performed via one of the analog IFFT / FFT precoder or one of the steerable miniaturized arrays in a port of the UCA, for improving one or more properties of steering one or more beams toward one or more intended destinations in the one or more LoS MIMO networks.

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for group formation and sounding for distributed multi-user multiple input multiple output (MU-MIMO). Some implementations include a method of wireless communication. The method includes transmitting, from a first access point of a plurality of access points, an announcement frame for performing a beamforming procedure for a distributed transmission. The distributed transmission includes a transmission from the plurality of access points. The announcement frame includes at least one identifier of a user terminal in a different basic service set than a basic service set of the first access point. The method further includes transmitting a packet for measuring a channel. The method further includes receiving feedback information from the user terminal based on the packet for measuring the channel. The method further includes transmitting the distributed transmission using the beamforming procedure.

A method and system for improving channel estimation for multi-user, multiple-input, multiple-output (MU-MIMO) systems through dynamic DM-RS port allocation in a 5G New Radio network is provided. The method comprises of allocating, by a Scheduler aware UE port allocation unit, one or more MU-MIMO ports to a plurality of code division multiplexing (CDM) groups for a plurality of UE users, performing, by a code selection unit, a code selection based on a number of allocated MU-MIMO ports, determining, by a spreading factor unit, a spreading factor allocated for each of the MU-MIMO ports in each of the plurality of CDM groups, determining, by a signalling unit, a Downlink Control Information (DCI) signalling of a Partial Port Occupancy (P-Poi) transmission for each UE and obtaining, by a channel estimation unit, channel estimates at the receiver through VSFOCC de-spreading based on the P-POI assigned for each of the MU-MIMO ports.

The present disclosure relates to a technique for enhancing downlink (DL) multi-user multiple-input multiple-output (MU-MIMO) reception for co-scheduled UEs by performing inter-user inference cancellation based on appropriately generated downlink control information (DCI). More specifically, in response to UE-related information indicating that a target UE is configured with other co-scheduled UEs for DL MU-MIMO reception, DCI is generated by a network node and transmitted to the target UE. The DCI indicates at least one group of co-scheduled UEs served by the network node for DL MU-MIMO reception, and also indicates a modulation scheme and / or a demodulation reference signal (DMRS) initialization value for each group of co-scheduled UEs of the at least one group of co-scheduled UEs. By using the DCI, the target UE may efficiently implement DL MU-MIMO reception with the co-scheduled UE.

The invention relates to a close-range correction method for a multiple-input-multiple-output ground-based synthetic aperture radar, and the method comprises the steps: obtaining original radar echo data and coupling interference signal data; performing digital cancellation processing on the original radar echo data and the coupling interference signal data; adjusting the multi-radar signal bandwidth, and performing at least ten times of measurement on the scene where the reference target is placed and the scene where the reference target is not placed; performing coherence superposition on the at least ten groups of radar data; performing digital cancellation processing on the coherently accumulated radar echo data with the reference target and the radar echo data without the reference target; carrying out band-pass filtering processing on the broadband echo data; determining correction data under broadband configuration; performing data fitting on the correction data under the broadband configuration; and obtaining the corrected radar original data. Therefore, the problems that a traditional MIMO-GBSAR system correction method is poor in robustness in a complex field environment and is difficult to effectively cope with system error time-varying characteristics are solved.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a first indication of a set of analog receive beams and an instruction to report one or more post-analog-beamformed channel impulse responses (CIRs) that are based at least in part on the set of analog receive beams. The UE may transmit a second indication of a CIR report that indicates the one or more post-analog-beamformed CIRs. Numerous other aspects are described.