58 results about "Basis expansion" patented technology

A method and system realize reliable wireless communications in non-coherent multiple-input multiple-output (MIMO) doubly-selective channels. The method uses Grassmannian space-time-frequency block codes and an iterative generalized likelihood ratio test (GLRT) with a multi-dimensional basis expansion model (BEM), decision reordering, and a fixed number of surviving candidates. The computational complexity of a non-coherent MIMO equalizer becomes linear as a function of a code length and a size of a modulation alphabet. The codebook, the alphabet size, the bit labeling, and the block power are optimized using worst-case channel statistics or instantaneous channel states. The method can use soft-information feedback from error correction codes, such as low-density-parity-check codes to improve performance.

The invention discloses an OFDM (orthogonal frequency division multiplexing) channel estimation method based on a symmetrical basis expansion model for a quick time-varying channel, and provides an estimation method for the quick time-varying channel. Under complex channel environments of high speed, medium speed, low speed and the like, existing channel estimation methods based on the basis expansion model have poor robust feature. Channel estimation based on the symmetrical basis expansion model is provided. A pilot frequency symbol Y(p) is extracted from a frequency domain receipt signal Y(k). According to the pilot frequency symbol Y(p), a basis coefficient vector corresponding to channel multipath time-domain impact response is estimated by the symmetrical basis expansion channel model. According to the coefficient vector, a time-domain channel matrix is calculated, so that the channel impact response is acquired, and channel estimation is completed. The method is low in complexity, and is a novel high-precision estimation method for the quick time-varying channel. The method can be used in various communication systems adopting OFDM to modulate so as to estimate the channel, and can also be used in a code division multiple access (CDMA) system and a time division multiple access (TDMA) system so as to estimate the channel.

The invention is a method for improved active sonar using a singular value decomposition filtering and a Volterra-Hermite Basis Expansion to model real active sonar measurements. The fitting model minimizes the sum of the squared errors between a measured channel response, z(t), and model response, y(t), which is a fitted Volterra Series solution. The model requires as input an excitation waveform, x(t), to which is fitted the model response, y(t). A contracted broadband cross-ambiguity function is used to correct the excitation waveform for Doppler and range effects. Once completed, the modeled response can be used to determine the linearity or non-linearity of the channel effects. Appropriate measures can be utilized to reduce these effects on the measured channel response.

A method and apparatus for analysis of molecular combinations featuring two or more molecular subsets is described. The computational method estimates the electrostatic affinity of the system via utilization of a basis expansion representing charge density and electrostatic potential functions associated with the first and second molecular subsets in a coordinate system. An electrostatic affinity, representing a correlation of the charge density and electrostatic potential functions of the first and second molecular subsets, is computed via suitable application of translation and rotation operators to the basis expansion coefficients over a sequence of different sampled configurations for the molecular combination. The method may also be combined with other methods for computation of shape complementarity in determining a composite or augmented score reflecting both electrostatic affinity and shape complementarity for configurations of a molecular combination.

A method and apparatus for analysis of molecular combinations featuring two or more molecular subsets is described. The method computes the shape complementarity of the system utilizing a basis expansion representing molecular shapes of the first and second molecular subsets in a coordinate system. The precomputed sets of translated expansion coefficients for the first molecular subset are first constructed via application of a translation operator to a reference set of expansion coefficients and then stored on a computer recordable medium for later retrieval. Then, a shape complementarity score, representing a correlation of the first and second molecular subsets, is computed via suitable application of rotation operators to both the stored translated expansion coefficients of the first molecular subset and the reference expansion coefficients for the second molecular subset over the sequence of different sampled configurations for the molecular combination.

The invention discloses a channel prediction method combining deep learning and a basis extension model in the technical field of wireless communication. The channel prediction method comprises the following steps of: step 1, acquiring a correlation matrix of a channel according to channel information at a historical moment; step 2, carrying out eigenvalue decomposition on the correlation matrix to obtain an optimal primary function; step 3, modeling a channel by using the basis expansion model; step 4, acquiring a basis coefficient estimation value based on historically received pilot signals and an optimal basis function; step 5, constructing a training sample set according to the basis coefficient estimation value; step 6, training a BP neural network by using the training sample set; step 7, acquiring a channel prediction model with an optimal weight and an optimal threshold value; step 8, performing online prediction based on the channel prediction model; and step 9, converting the basis coefficient estimation value into a frequency domain channel matrix. The channel prediction method has low calculation complexity and high prediction precision, and is suitable for efficient acquisition of time-varying channel information in a high-speed mobile environment in the future.