290 results about "Multi output" patented technology

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.

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.

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.

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 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.

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.

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.

Current approaches for minimizing energy requirement of buildings are not designed to handle multi-input multi-output systems, such as electric vehicle-heating, ventilation, and air conditioning (EV-HVAC) system. Further, scalability of the solutions is another challenge. Present disclosure provides method and system for jointly controlling EV-HVAC system of a building. The system utilizes the potential of electric vehicle (EVs) in building energy management by treating EVs as buffers with random availability. The system performs EV-HVAC joint control that scales seamlessly with increasing EVs while respecting both thermal constraints of HVAC and state of charge (SoC) constraints of EV users.

Various techniques pertaining to Ultra High Reliability (UHR) coordinated beamforming (CBF) joint physical-layer protocol data unit (PPDU) transmission with UHR downlink (DL) multi-user multiple-input-multiple-output (MU-MIMO) PPDU format in wireless communications are described. An apparatus (e.g., a first access point (AP) ) participates in a UHR joint PPDU transmission sequence with a second AP by: (a) transmitting a trigger frame (TF) to the second AP to trigger a coordinated beamforming PPDU transmission; or (b) receiving the TF from the second AP. The apparatus also transmits a first CBF joint PPDU to one or more stations (STAs) associated with the first AP which is synchronized with a second CBF joint PPDU transmitted by the second AP to one or more STAs associated with the second AP. Each of the first and second CBF joint PPDUs is transmitted in a downlink (DL) multi-user multiple-input-multiple-output (MU-MIMO) PPDU format.

A four-degree-of-freedom piezoelectric stick-slip micro-nano manipulator belongs to the technical field of precision operation and comprises a three-degree-of-freedom piezoelectric stick-slip motion platform and a single-degree-of-freedom puncture device. The three-degree-of-freedom piezoelectric stick-slip motion platform comprises a multi-output driving mechanism, a constraint pre-tightening mechanism, a base and an output platform. The multi-output driving mechanism is mounted on the base to realize four-stage amplification of the displacement of the output platform, and the output platform is supported by the base; and the constraint pre-tightening mechanism is mounted on the base through the multi-output driving mechanism and is used for constraining the z-axis positive direction displacement of the output platform. The single-degree-of-freedom puncture device is installed on the output platform, the multi-output driving mechanism drives the single-degree-of-freedom puncture device to move along the x-axis, move along the y-axis and rotate around the z-axis through the output platform, and the single-degree-of-freedom puncture device achieves displacement three-level amplification puncture operation under the action of driving voltage. The micro-nano manipulator has the characteristics of large stroke, high precision, multi-degree-of-freedom parallel decoupling, large single-step displacement and high load.

Various aspects of the present disclosure generally relate to wireless communication. Some aspects more specifically relate to inter-user-equipment (inter-UE) interference cancellation (IC) for physical downlink control channel (PDCCH) multi-user multiple-input multiple-output (MU-MIMO). In some aspects, a user equipment (UE) may be configured with an indication of cell radio network temporary identifiers (C-RNTIs) and / or scrambling code identities of respective candidate UEs that use the same or overlapping control-channel elements (CCEs) as the UE. In some aspects, the UE may perform the inter-UE IC by decoding a PDCCH communication targeted for one or more of the candidate UEs and subtracting the decoded PDCCH communication from at least a part of a PDCCH MU-MIMO communication such that the remaining signal includes data targeted for the UE.

Methods, systems, and devices for wireless communication are described. The described techniques relate to various scheduling procedures for wireless communications. In some implementations, a user equipment (UE) may estimate transmit power used within a duration for tracking specific absorption rate (SAR) and may indicate the estimated transmit power to a network. The network may schedule UEs based on the estimated transmit power. In some implementations, a UE may receive a control message including an uplink grant and an indication that monitoring of a physical downlink control channel (PDCCH) is skipped during a discontinuous reception cycle (DRX) on duration. The UE may send a message to accept, in part accept, or reject skipping of PDCCH monitoring. In some implementations, a UE may be configured for multi-user multiple-input multiple-output (MU-MIMO) communication with other UEs. A UE may receive a message indicating parameters of other UEs.

Techniques for calibrating in-band and out-of-band receivers using complex signals. The techniques include receiving a signal from a wireless radio antenna at a first receiver and a second receiver in a wireless communication system. The first receiver is configured to receive in-band transmission during operation of the wireless communication system and the second receiver is configured to receive out-of-band transmission during operation of the wireless communication system, and the signal is transmitted at least partially simultaneously from the wireless radio antenna to the first receiver and the second receiver. The techniques further include determining a difference of complex signals for the first receiver and the second receiver based on measuring the signals received from the wireless radio antenna at the first receiver and the second receiver, and calibrating the second receiver with the first receiver based the difference of complex signals.

This application discloses a multi-inductor multi-output DC-DC voltage converter and its control method. The converter includes several basic unit modules and a charge redistribution module. The basic unit modules are connected in series and all are connected to the charge redistribution module. The method includes: based on the input voltage signal, and using the basic unit modules, sequentially and alternately magnetizing and demagnetizing a first inductor within a complete cycle to obtain the load-side output current and load-side output voltage; obtaining the first flying capacitor current and combining it with the load-side output current and load-side output voltage; and adjusting the inductor current based on the charge redistribution module to output the adjusted inductor current. The embodiments of this application enhance the ability to support higher input voltages and more output channels while reducing external components, thereby improving system conversion efficiency. This application can be widely applied to integrated circuit design technology.

A method is disclosed of selecting users for multi user multiple-input multiple-output (MU-MIMO) communication from an initial set of potential users. The method comprises repeating the following steps in successive iterations until a stopping criterion is met: determining a channel correlation metric for each user in the set of potential users, reducing the set of potential users by exclusion of a user based on the channel correlation metric, and calculating a performance metric of the set of potential users. The method also comprises selecting users corresponding to one of the sets of potential users, wherein the selection is based on a comparison of the calculated performance metrics of the sets of potential users. The channel correlation metric for a user may comprise one or more of: a channel filter norm for the user, a channel norm for the user, a channel gain for the user, pair-wise correlations between the user and one or more other users of the set of potential users, and a channel eigenvalue for the user. The performance metric may comprise one or more of: a sum-rate, a per-user-rate, an average error rate, a maximum error rate, a per-user error rate, and a sum-correlation. Corresponding apparatus, network node and computer program product are also disclosed.

Methods, media, and systems are provided for facilitating intelligent pairing decisions in a multi-user (MU) multiple-in-multiple-out (MIMO) system. Device information corresponding to one or more devices served by the fixed wireless access (FWA) device is received from the FWA device. Based on the device information, it is determined whether the FWA device is a candidate for pairing with another device for MU-MIMO. If it is determined that the FWA device is a candidate for pairing, the FWA device is paired with a pairing device, and a set of signals is transmitted using the MU-MIMO to the FWA device and the pairing device. If it is determined that the FWA device is not a candidate for pairing, a set of signals is transmitted to the FWA device without use of the MU-MIMO.

The present application belongs to the technical field of unmanned aerial vehicle navigation, and particularly relates to an online trajectory prediction and parameter optimization method coupling multiple output Gaussian processes. The method first constructs a multiple output coupling kernel function fusing a square exponential kernel and a neural network kernel, and uses a symmetric positive definite output covariance matrix to explicitly model the dynamic coupling relationship between the lateral position (axis) and the longitudinal position (axis) of the unmanned aerial vehicle, thereby overcoming the coupling information loss caused by the traditional independent modeling method. Secondly, a recursive Gaussian process online learning framework is established, and an augmented state space model containing the flight trajectory latent function value and the kernel hyperparameter is constructed. Finally, the unscented transformation technology is introduced to process the nonlinear propagation of the hyperparameter, and the real-time prediction of the flight state of the unmanned aerial vehicle and the online adaptive update of the model hyperparameter are realized through the block Kalman filtering mechanism. The present application significantly improves the precision, real-time performance and robustness of the unmanned aerial vehicle trajectory prediction under complex flight environment and high maneuvering task.