556 results about "MIMO" patented technology

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

Aspects presented herein may improve the performance of a JCS system by providing various resource mapping for the sensing and communication of the JCS system. Aspects presented herein may enable a base station to use PDSCH for MIMO sensing with FDM based waveform orthogonality, and may also enable a UE to support DMRS channel estimation when PDSCH is used for MIMO sensing with FDM based waveform orthogonality. In one aspect, a UE receives a plurality of PDSCHs on a plurality of ports, each PDSCH of the plurality of PDSCHs being received through a different port of the plurality of ports, each PDSCH of the plurality of PDSCHs being received on a different set of non-overlapping REs within each RB of a set of RBs. The UE demodulates each PDSCH of a port based on DMRS received through the port.

The invention provides a multi-antenna MIMO architecture synchronization method and device, a program product and a storage medium, and relates to the technical field of wireless communication. Receiving sampling signals through a plurality of receiving channels; performing down-sampling on each sampling signal to obtain a down-sampling signal; determining a plurality of cache regions to store each downsampling signal, and performing cross-correlation operation on each downsampling signal and a local synchronous time domain signal; when any receiving channel detects an effective peak value, determining the position of the effective peak value as a preliminary synchronization position; determining a corresponding search window range, and performing weighted stacking on the target cross-correlation results to obtain an overall correlation peak value; searching a maximum value in all the total correlation peak values, and determining a position corresponding to the maximum value as a final synchronization position; and calculating a frequency offset estimation value of each receiving channel according to each pilot signal, and carrying out weighted average on each frequency offset estimation value to obtain a final frequency offset estimation value. By adopting the method, the wireless frame synchronization precision can be improved.

The invention provides a method for assisting a movable antenna to realize secure communication by STAR-RIS, and relates to the technical field of wireless communication. According to the method, the safety and the rate of the system are maximized by jointly optimizing the beam forming of the base station, the position of the movable antenna and the STAR-RIS coefficient matrix. The method specifically comprises the following steps: constructing a downlink MIMO system and establishing a joint optimization problem; decomposing the joint optimization problem into three sub-problems by adopting an alternate optimization framework; respectively utilizing a particle swarm optimization algorithm and a weighted minimum mean square error method to be combined with a Majoritization-Minimalization algorithm to solve each sub-problem, and respectively utilizing the particle swarm optimization algorithm and the weighted minimum mean square error method to be combined with the Majoritization-Minimalization algorithm to solve each sub-problem; and alternately executing related steps until system security and rate convergence to obtain a solution of the joint optimization problem. By the adoption of the method, the safety communication performance of the system is remarkably improved, full-space coverage and precise beam regulation are achieved, the algorithm convergence is high, the solving efficiency is high, and the method is suitable for a downlink system with multiple users and multiple eavesdropping nodes.

The present invention relates to the technical field of wireless communications. Disclosed are a deep learning-based CSI compression feedback method and system for a multi-antenna MIMO system. The method comprises: acquiring raw CSI data, and preparing a neural network model training set; on the basis of the acquired data, constructing a U-Net neural network model integrated with a Transformer; training the neural network model offline to obtain a trained neural network model; and performing metric evaluation on the trained neural network model. The present invention improves the CSI compression efficiency and reconstruction accuracy of the multi-antenna MIMO system, accelerates data processing and feedback speed, enhances model adaptability, and achieves accurate restoration of compressed data. In addition, the multi-scale convolution and skip connection design of the network preserves more details, thereby improving reconstruction quality. An optimized network structure not only reduces the amount of calculation, but also improves processing speed, so that the model can adapt to various channel environments and communication scenarios.

A base station may perform a beamforming for a UE group including UEs that are disposed spatially close to each other for MU-MIMO. The base station may generate group common DCI including a beam indication and / or a PL-RS indication for the UE group. The ID included in the group common DCI may be group specific for the UE group or UE specific for a specific UE. The beam indication for different channels may be indicated by an RNTI or an RRC.

The invention relates to a 92GHz MIMO roadside radar radio frequency system based on a frequency conversion assembly, and the radar radio frequency system comprises a baseband processing layer which comprises at least two cascaded millimeter wave system-level SoC chips; the frequency conversion layer is a frequency conversion module FCM, and the FCM comprises a local oscillator synthesis unit, an upper mixer group and a lower mixer group; the local oscillation synthesis unit is used for generating a 13GHz local oscillation signal; the input end of the upper mixer group is connected with the output end of the baseband processing layer, the local oscillation end of the upper mixer group is connected with the local oscillation synthesis unit, the output end of the lower mixer group is connected with the input end of the baseband processing layer, and the local oscillation end of the lower mixer group is connected with the local oscillation synthesis unit; and the down-conversion layer is used for down-converting 92-94 GHz echo signals received by the antenna array to 79-81 GHz and then outputting the echo signals to the baseband processing layer. According to the invention, 92-94 GHz frequency conversion can be realized.

A microstrip antenna which includes a substrate, a ground on a second side of the substrate, a first patch on a first side of the substrate, and a second patch on the first side of the substrate. The first patch is connected to a first port. The second patch is separated from the first patch and connected to a second port. Each of the first and second patches is further formed with a plurality of shorting vias connected to the ground. The radiation patterns of each element also feature the RPD characteristic, which is promising for large-scale MIMO or array antennas.

A method of wireless communication includes determining a first precoding matrix indicator (PMI) value and a second PMI value, the first PMI value indicating a first matrix W1 of a first codebook C1, the second PMI value indicating a second matrix W2 of a second codebook C2, wherein the first matrix W1 and the second matrix W2 together define a precoding matrix W for multi-antenna transmission. The method also includes transmitting the first PMI value at a first periodicity, and transmitting the second PMI value at a second periodicity shorter than the first periodicity. In some examples, the method also includes transmitting a rank indicator (RI) value, where the first PMI value and the second PMI value are determined based on the RI value.

The invention relates to the technical field of communication, and particularly discloses a front-end communication circuit and a communication method, and the front-end communication circuit comprises a communication chip, a first external change-over switch and a second external change-over switch. The communication chip is provided with a first transceiver interface, a second transceiver interface and a third transceiver interface. The first external change-over switch is configured to selectively route a middle-high frequency band signal to the first transceiving interface or allow the signal to be input through the second transceiving interface or the third transceiving interface; meanwhile, the second external change-over switch is used for switching the MIMO radio frequency signal to the second transceiver interface or the third transceiver interface. All the selected signals are finally input into the communication chip through corresponding interfaces to be processed. According to the scheme, flexible frequency band combination and carrier aggregation configuration can be supported so as to meet the differentiation requirements of global 5G frequency bands. In addition, through dynamic configuration of an external switch and an interface, a single hardware design can be compatible with module schemes of multiple domestic semiconductor manufacturers, and repeated development investment is avoided.

The invention discloses a GNN signal detection method based on an attention mechanism in a cellular-removed large-scale MIMO system, and belongs to the technical field of wireless communication. Aiming at the problem that the traditional EP algorithm adopts independent Gaussian approximation for multi-user posterior distribution and cannot effectively capture interference association among users, the method is combined with a graph neural network and a 2D MHA module, the 2D MHA module captures the interference relation between users through the time attention sub-block, the frequency attention sub-block captures the correlation between different features, the two outputs are spliced and subjected to linear projection to form enhanced feature representation, and meanwhile the fusion capacity of prior and likelihood information is enhanced through the EP module. According to the method, the low calculation complexity is kept while the detection precision is improved, and the adaptability and robustness of the algorithm in different channel and network environments are remarkably enhanced.

The invention discloses a low-energy-consumption resource allocation method based on multi-agent deep reinforcement learning in a cellular-free large-scale MIMO system, and the method comprises the steps: taking the minimization of the total energy consumption of all users as a target, and building a joint optimization problem of a user task unloading strategy, an uplink power allocation strategy and a computing resource allocation strategy; the optimization problem is converted into a deep reinforcement learning model, the user, the access point and the cloud server are respectively converted into intelligent agents, the state, the action and the total reward function of each intelligent agent are set, the actions of the intelligent agents of the user are set to be a user task unloading strategy and an uplink power distribution strategy, and the total reward function of the intelligent agents of the user is set to be a total reward function. Actions of agents of the access point and the cloud server are set as a computing resource allocation strategy, and a total reward function is set as an inverse number of total energy consumption; and solving the deep reinforcement learning model to obtain an optimal computing resource allocation strategy. According to the invention, joint optimization of unloading, power allocation and resource allocation is realized.

The invention relates to a resource optimization method of an active RIS enabled cellular-free MIMO-SWIPT system. The resource optimization method comprises the following steps: S1, constructing a system model and a performance index expression of active RIS enabled cellular-free large-scale MIMO in an SWIPT scene; s2, constructing an optimization problem of joint resource allocation according to the performance index expression; and S3, equivalently converting the optimization problem into an alternating optimization problem through a weighted MMSE (Minimum Mean Square Error) method, and iteratively solving the alternating optimization problem through an alternating optimization framework. The method not only effectively overcomes the restriction of the double fading effect on the system performance, but also solves the problem of synchronous amplification of interference and noise when the active RIS amplifies the desired signal, and obviously improves the downlink weighting sum rate of the system.

The invention discloses a single-loop droop control method and system for a wind generating set, and the method comprises the following steps: constructing a doubly-fed fan-power converter-power grid three-module coupling model, and completing the dynamic adaptation of the characteristics of a doubly-fed fan of a single-loop droop control architecture without inner-loop current control; based on the input signal-model collaborative integration body and the adaptive architecture, MIMO-GBC is adopted to analyze a phase margin and a resonance peak value of an open-loop transfer function matrix eigenvalue of a dynamic small signal model, and double risks are identified in a full slip rate interval; aiming at the resonance risk, a current induction type virtual impedance strategy is adopted; aiming at insufficient phase margin, adopting a transient phase calibration strategy; and according to the real-time slip ratio of the DFIG and the stator power change, inter-partition linkage optimization is carried out on the virtual resistance and the phase calibration angle so as to minimize a dual-power step response fluctuation square mean value, a parameter setting period and a rotor side converter control period are synchronously matched, and all-working-condition stable control is realized.

A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an MIMO selection service. The MIMO selection service may include calculating MIMO configurations that include a number of layers for each MIMO configuration pertaining to a group of end devices; and identifying performance metric sensitive end devices of the group. The MIMO selection service may further include selecting for each performance metric sensitive end device, a first MIMO configuration from the MIMO configurations that has a higher number of layers relative to one or more other MIMO configurations of the MIMO configurations; and selecting for each non-performance metric sensitive end device of the group, a second MIMO configuration from the MIMO configurations that combined with the first MIMO configuration yield a highest spectral efficiency.

A wireless communication device includes a processor to determine signal characteristics of digitized down-converted signals across first intermediate frequency band and second intermediate frequency band based on spatial analysis on the digitized down-converted signals. The processor perform cross-band correlation between the digitized down-converted signals across the first intermediate frequency band and the second intermediate frequency band. The processor detects jamming signal and jammer type in digitized down-converted signals based on cross-band correlation and determined signal characteristics of the digitized down-converted signals. The processor applies spatial nulling to multiple-input-multiple-output (MIMO) antenna array to dynamically suppress jamming signal when jammer type is spatial jammer. The processor activates jammer defense function to switch communication to unaffected intermediate frequency band when jammer type is non-spatial jammer and jamming signal is detected on primary intermediate frequency band and perform cross-band validation on digitized down-converted signals to confirm mitigation success of jamming signal.

The invention discloses a high-isolation broadband four-port dual-polarized antenna based on differential and common modes and a communication terminal. Comprising a feed layer, a lower dielectric layer, a metal floor, an air layer, an upper dielectric layer and a radiation patch which are stacked in sequence. The feed layer is located on the lower surface of the lower dielectric layer and comprises a feed connector, a micro-strip feeder line, a lumped capacitor, a lumped inductor, a feed metal column and a short circuit metal column. The metal floor is located on the upper surface of the lower dielectric layer, and a plurality of circular gaps concentric with the feed metal columns are etched in the metal floor. The radiation patch is located on the upper surface of the upper dielectric layer, is composed of a horizontal rectangular metal, a vertical rectangular metal and an inclined metal branch, and is used for improving the isolation between different feed ports. The antenna has the characteristics of simple structure, wide band, high isolation and dual polarization, and is suitable for application scenes such as multiple-input-multiple-output (MIMO) systems, radars, satellites and base stations.

Apparatuses, methods, and systems are disclosed for calculating an EVM of a transmitter. One apparatus includes a processor and a receiver that receives a multiple-layer MIMO signal from a transmitter via a propagation channel. The processor measures the received multiple-layer MIMO signal using an unbiased linear MMSE MIMO equalizer and calculates an EVM of the transmitter, where the EVM for each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the unbiased linear MMSE MIMO equalizer.

The invention relates to the technical field of wireless communication, and discloses a sparse channel estimation method and system for 5G-A5G-A super-large scale MIMO. The system comprises a quantitative sensing preprocessing module, a layered sparse reconstruction module, a high-precision guide correction module and a dynamic threshold decision module, and realizes efficient and accurate recovery of channel sparse characteristics under strong quantitative interference through nonlinear distortion compensation, multi-resolution sparse recovery, high-precision reference signal guide correction and parameter adaptive adjustment. The estimation performance is improved; and the calculation complexity is balanced.

A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an MIMO selection service. The MIMO selection service may include calculating MIMO configurations that include a number of layers for each MIMO configuration pertaining to a group of end devices; and identifying performance metric sensitive end devices of the group. The MIMO selection service may further include selecting for each performance metric sensitive end device, a first MIMO configuration from the MIMO configurations that has a higher number of layers relative to one or more other MIMO configurations of the MIMO configurations; and selecting for each non-performance metric sensitive end device of the group, a second MIMO configuration from the MIMO configurations that combined with the first MIMO configuration yield a highest spectral efficiency.

The invention relates to the technical field of intelligent monitoring equipment, in particular to a radar point cloud target detection system and method based on multi-modal space-time fusion. Comprising the following steps: at least two radar transceivers synchronously collect echo signals of a target at different visual angles, and establish a three-dimensional coordinate system; based on the three-dimensional coordinate system of the target points, the target points of each time slice are clustered, a coordinate set of point cloud clustering is obtained, and whether the point cloud gravity center of each detection target is displaced or not is judged; if the detected target is displaced, capturing a space trajectory of a target point based on the barycentric coordinates, and performing gait analysis on the detected target; and if the detection target does not displace, judging the body posture of the detection target. The method has the advantage of low cost, reliable radar point cloud can be realized, the defects that the traditional point cloud needs multi-antenna MIMO and the cost is high are overcome, body point cloud modeling in the walking process is realized, meanwhile, posture body point cloud and vital sign data are jointly verified, and the reliability is enhanced.

The invention discloses a multi-user joint decoding method for a cellular-free large-scale MIMO system based on model-driven deep learning, and the method comprises the steps: building an optimization problem of a simulation combination matrix with maximization of the reachable rate of the cellular-free large-scale MIMO system as a target in a simulation part of data decoding; on the basis of a heuristic algorithm, an optimization target is simplified, an optimization problem is divided into a plurality of sub-problems, the single AP simulation precoding matrix optimization problem is solved one by one, and an optimal simulation combination matrix is obtained; a multi-user joint LDPC decoding algorithm is expanded to a cellular-free large-scale MIMO scene, multi-user joint LDPC decoding is realized based on an MIMO-LDPC-Net decoding network, an input layer of the MIMO-LDPC-Net decoding network generates an initial value as an input of a hidden layer, the hidden layer is formed by stacking modules composed of a first sub-layer, a second sub-layer and an MIMO detection layer, and the first sub-layer, the second sub-layer and the MIMO detection layer are stacked. The output layer generates a log-likelihood ratio of the variable node. Experiments show that compared with a traditional algorithm, the heuristic algorithm and the AI auxiliary joint decoding algorithm have a signal-to-noise ratio gain of 1dB.

The application discloses a beacon user selection method for refining VR boundary in an XL-MIMO system, and comprises the following steps: selecting part of users from a candidate user set A to form an initial beacon user set B , obtaining the estimated value of the VR label of the rest of the users in B according to the distance relationship; dividing all the users in A into several non-overlapping subsets according to the VR label, so that each subset corresponds to an estimated VR region, calculating the coordinate center point of each VR region and the distance between the beacon point in the region and the center point; if the distance between a user in B and any VR region center point is less than A , and the estimated value of the VR label of the user is different from the VR label of the region, then the user can be selected as a beacon user for refining the VR boundary and added to B ; the above process is repeated until all the beacon users are selected. The application can effectively solve the problem of low VR recognition efficiency caused by too few boundary beacon users in the XL-MIMO system, and make the distribution structure of the beacon users more reasonable.

Embodiments of the present disclosure are related an open radio access network (ORAN) based base station (BS). The BS comprising at least one radio unit (RU) comprising a plurality of antenna ports, at least one distributed unit (DU) and an interface for a communication between said RU and said DU. The RU is configured to support one or more virtual sub-sectors simultaneously, wherein each virtual sub-sector cover a pre-defined geographical area in both horizontal and vertical directions. Each virtual sub-sector is associated with a specific set of antenna ports of the RU. Each virtual sub-sector provides a coverage for one of distinct geographical areas and partially overlapping geographical areas. The DU associates a UE with a virtual sub-sector. The DU performs at least one of a SU-MIMO within a virtual sub-sector for a UE associated with said virtual sub-sector, and a MU-MIMO across the UEs in different virtual sub-sectors.

The application provides a uniform plane array FDA-MIMO three-dimensional target positioning method and system based on complete decoupling atomic norm minimization. The method converts the problem into a semi-positive programming solution by constructing a low-dimensional observation matrix and using complete decoupling atomic norm, and realizes joint estimation of distance, pitch angle and azimuth angle through Vandermonde decomposition. Compared with high complexity algorithms (VANM, etc.), the application significantly reduces the calculation amount by decomposing the Toeplitz matrix, and improves the single snapshot precision by using the multi-slice constraint mechanism. Compared with low complexity algorithms (3D-DANM), the application introduces an auxiliary variable matrix to restore the distance-angle coupling structure, avoids the performance loss caused by excessive relaxation, and significantly improves the estimation accuracy and multi-target resolution capability while ensuring the calculation efficiency.

The application provides a hybrid user unlicensed random access method in a cell-free massive MIMO, comprising the following steps: deploying a cell-free massive MIMO system and calculating an optimal backoff parameter; after obtaining the optimal backoff parameter, an access point broadcasts the optimal backoff parameter to all users; the users randomly activate according to the optimal backoff parameter broadcast by the access point; the users successfully activated randomly select a pilot from a predefined pilot pool and send the pilot to the access point; the access point judges whether the signal-to-interference ratio of the user with the maximum signal strength among the users selecting the same pilot is greater than a threshold value; if yes, the access point identifies the user with the maximum signal strength and accesses the user; otherwise, all the users in conflict are considered to have failed to access and wait for the next reinitiation of access. The application can significantly improve the problem of poor performance of edge users, improve the access efficiency of various types of users in an Internet of Things scene, has high feasibility and can be applied to an actual communication scene.

The application provides a Massive MIMO channel state transmission method based on mixed partition and complex value network, and the method mainly comprises two parts: the first part provides a channel environment mixed partition method. Through the double mixed partition technology based on position information and channel characteristics, the method can effectively reduce the model complexity, significantly improve the system efficiency, scalability and overall performance, and reduce the calculation overhead of the user equipment. The second part provides a lightweight complex neural network model, which can fully exploit and utilize the complex value characteristics of the channel state, and the lightweight design ensures the high training efficiency and inference efficiency of the model. Combined with the above two methods, the application realizes higher precision and efficiency of channel state feedback transmission compared with the traditional technology.

A computer-implemented method, performed in a network node for activating or deactivating transmission of a MIMO data stream in a LOS-MIMO system, from a first site to a second site, the method comprisingconfiguring a nominal output power setting of a transmitted MIMO data stream at the first site, obtaining a request for activation of an additional MIMO data stream,configuring a reduced output power setting at the first site below the nominal output power setting, andinitially transmitting the additional MIMO data stream at the reduced output power setting from the first site.

The application relates to the technical field of space optical communication, and discloses a MIMO optical transmission system for a cluster unmanned aerial vehicle and a method thereof, which comprises a flight control computer, a transmitting array, a receiving array and a baseband processing unit. The baseband processing unit acquires the three-dimensional position and attitude matrix of a transceiving node, carries out coordinate system transformation, screens out candidate transmitting arrays satisfying geometric alignment conditions, reconstructs an aerodynamic wake turbulence field in combination with a motor rotating speed parameter, carries out integral of refractive index disturbance along a path to eliminate arrays blocked by air flow, drives a flight control loop to perform attitude biasing when the effective array is limited, carries out channel optimization, then splits data into spatial mapping bits and light intensity modulation bits, selects a specific array to send optical pulses, and the receiving end uses a lens to carry out spatial isolation and focusing on incident light beams, and demodulates and restores data in reverse according to the photoelectric current characteristics. The application realizes feedforward avoidance of complex air flow interference and dynamic optimization of limited channels, reduces line transmission crosstalk, and guarantees communication quality.