11 results about "Mimo channel matrix" patented technology

Certain aspects of the present disclosure provide techniques for wireless communications by an apparatus. Certain techniques include receiving signals corresponding to a MIMO channel matrix; generating a first gram matrix from a basis for a first lattice corresponding to a first signal of the received signals; providing the first gram matrix to a neural lattice reduction model comprising an equivariant neural network configured to generate a current extended Gauss move; generating, with the neural lattice reduction model, a current partial changed basis based on the current extended Gauss move and the basis; executing one or more additional iterations of the neural lattice reduction model; and demapping the MIMO channel matrix based on combining the current partial changed basis and each of the additional partial changed basis generated by each of the one or more additional iterations of the neural lattice reduction model.

The invention relates to a power line communication transmission method, device and system, and the method comprises the steps: firstly obtaining an MIMO channel matrix of a target subcarrier in a power line communication system, then determining a quality evaluation parameter of the target subcarrier according to the channel matrix of the target subcarrier, and then determining a mode matching threshold according to the quality evaluation parameter and the mode matching threshold. And determining a diversity multiplexing mode of the target subcarrier, and finally controlling a sending end to carry out subcarrier mapping and signal transmission processing on the target subcarrier according to the diversity multiplexing mode of the target subcarrier. Namely, by dynamically evaluating the subcarrier quality and adjusting the diversity multiplexing mode in real time, the problem that the prior art cannot adapt to the rapid change of the power line channel is solved, and the method has the advantage of improving the transmission reliability and the rate adaptability of the power line communication system in the dynamic channel environment.

Certain aspects of the present disclosure provide techniques for wireless communications by an apparatus. Certain techniques include receiving signals corresponding to a MIMO channel matrix; generating a first gram matrix from a basis for a first lattice corresponding to a first signal of the received signals; providing the first gram matrix to a neural lattice reduction model comprising an equivariant neural network configured to generate a current extended Gauss move; generating, with the neural lattice reduction model, a current partial changed basis based on the current extended Gauss move and the basis; executing one or more additional iterations of the neural lattice reduction model; and demapping the MIMO channel matrix based on combining the current partial changed basis and each of the additional partial changed basis generated by each of the one or more additional iterations of the neural lattice reduction model.

Systems, methods, and instrumentalities are described herein in association with group sparsity and implicit regularization for MIMO channel estimation. For example, a channel matrix (e.g., MIMO channel matrix) may have a group sparse property. The group sparse property may be used in channel estimation applications. For example, the group sparse property in MIMO channel matrices may be used with non-convex operations to perform matrix completion. Matrix completion may be performed to determine an unknown channel matrix, for example, using limited entries (e.g., noisy entries).

Certain aspects of the present disclosure provide techniques for wireless communications by an apparatus. Certain techniques include receiving signals corresponding to a MIMO channel matrix; generating a first gram matrix from a basis for a first lattice corresponding to a first signal of the received signals; providing the first gram matrix to a neural lattice reduction model comprising an equivariant neural network configured to generate a current extended Gauss move; generating, with the neural lattice reduction model, a current partial changed basis based on the current extended Gauss move and the basis; executing one or more additional iterations of the neural lattice reduction model; and demapping the MIMO channel matrix based on combining the current partial changed basis and each of the additional partial changed basis generated by each of the one or more additional iterations of the neural lattice reduction model.

The invention discloses a method for testing and verifying an uplink subsystem based on a multi-antenna environment, which relates to the technical field of wireless communication and comprises the following steps: measuring phase noise by using a high-precision analyzer, acquiring an MIMO channel matrix by using field equipment, estimating a channel state based on a minimum mean square error, eliminating multi-user interference by using zero-forcing detection, and verifying the uplink subsystem based on the zero-forcing detection. A non-ideal channel is pre-coded and optimized by using a codebook, an error, an error rate and gain change are graphically displayed, and a simulation curve assists in optimizing uplink configuration. According to the invention, the test verification precision and efficiency of an uplink subsystem in a multi-antenna environment are effectively improved, and accurate estimation of a channel state and effective suppression of multi-user interference are realized by finely processing phase noise and MIMO channel information. The system performance is visually presented through graphical display and simulation curve assistance, the problem can be rapidly positioned, uplink configuration is optimized, and further development of the wireless communication technology is promoted.

The present invention relates to a MIMO communication system signal detection method, circuit, device, and apparatus based on a memristor array, which are used in the field of wireless communication signal processing. To address the problem that existing MIMO signal detection calculation circuits based on memristor arrays are extremely sensitive to memristor conductance errors, the present invention represents the MIMO channel matrix as the product of a small-scale fading coefficient matrix and a large-scale fading coefficient matrix, transforms the matrix calculation expressions for ZF signal detection and MMSE signal detection into a new equivalent form, and uses a memristor array-based matrix calculation circuit to calculate the transformed equivalent expression to achieve signal detection. The present invention provides two memristor array-based matrix calculation circuit design schemes, two corresponding MIMO communication system signal detection devices, and three memristor array-based MIMO communication system signal detection devices. The present invention is less sensitive to memristor conductance errors and has better signal detection performance.

The invention relates to a laminated intelligent metasurface-assisted low-altitude holographic MIMO channel optimization fitting method, and belongs to the technical field of digital communication. The method comprises the following steps: initializing system parameters, and determining beam forming matrixes of TX-SIM and RX-SIM; constructing a spatial correlation matrix of a transmitting and receiving end, and determining a spatial correlation holographic MIMO channel matrix, a singular value matrix and a scaling factor of an end-to-end channel; calculating channel fitting errors corresponding to a plurality of groups of preset super-atom phase shift configurations; and by taking minimization of a channel fitting error as an optimization target, carrying out iterative optimization on the super-atom phase shift by adopting a linear decline particle swarm optimization method, and obtaining and outputting optimal phase shift configuration. According to the method, the optimization problem caused by multilayer coupling and non-convex constraint of the laminated intelligent metasurface in a low-altitude dynamic environment is effectively solved, the global optimization capability is enhanced, the channel fitting error is reduced, and the channel capacity and the communication quality of a holographic MIMO system are improved.

The application relates to the technical field of MIMO-PLC systems, in particular to a joint precoding and power allocation method and device suitable for a MIMO-PLC system, the method comprising determining effective channel eigenvalues and corresponding effective eigenmodes of each subcarrier according to a MIMO channel matrix and a channel eigenvalue threshold, constructing a truncated precoding matrix of each subcarrier based on the effective eigenmodes, performing power allocation based on the effective channel eigenvalues of each subcarrier and a preset fixed gap, and obtaining the bit number corresponding to each effective eigenmode; the method introduces a fixed gap constraint of a discrete constellation diagram, converts an originally non-convex discrete rate maximization problem into a low-complexity water-filling power allocation problem, and accordingly performs truncated singular value decomposition, thereby effectively guaranteeing system throughput and bit error rate performance while reducing calculation complexity, and a good balance between algorithm complexity and transmission performance is achieved.

This invention relates to a MIMO channel estimation method based on a diffusion model and expectation propagation under low-resolution quantized observations. It involves acquiring a quantized observation matrix, a pilot matrix, and historical channel data. A denoising diffusion implicit model is employed, and the prior distribution of the MIMO channel matrix is ​​learned unsupervised through forward noise addition to obtain the channel prior score function. To address the nonlinear constraints introduced by the quantized observation matrix, a posterior distribution satisfying the quantization interval constraints is constructed, and the expectation propagation algorithm is used to iteratively approximate it as a Gaussian distribution, calculating the likelihood gradient. Combined with the prior score function, the likelihood gradient is integrated into the inverse sampling process of the denoising diffusion implicit model. Using the quantized observation matrix and pilot matrix as guiding conditions, the initial noise is gradually sampled to a channel estimation result consistent with the quantized observations through gradient guidance. Compared with existing technologies, this invention has advantages such as high accuracy, strong versatility, and good stability.

According to the present disclosure, there are provided methods and devices for estimating a high-dimensional MIMO channel by expressing the MIMO channel in the form of a flow function to determine a change from an input state to an output state. This may be considered similar to a manner in which fluid dynamic problems are solved. Instead of using linearization to estimate all of the coefficients of a matrix representing a MIMO channel, a receiver may determine the MIMO channel rank by detecting leading modes in a subspace of the entire MIMO channel matrix.