Abstract:
A sliding window based data estimation is performed. An error is introduced in the data estimation due to the communication model modeling the relationship between the transmitted and received signals. To compensate for an error in the estimated data, the data that was estimated in a previous sliding window step or terms that would otherwise be truncated as noise are used. These techniques allow for the data to be truncated prior to further processing reducing the data of the window.
Abstract:
A receiver which suppresses inter-cluster multipath interference by processing an impulse channel response consisting of two multipath clusters, each cluster having groups of signals with multiple delays. In one embodiment, the receiver includes a single antenna and parallel-connected delay units used to align the groups of signals before being input into respective sliding window equalizers. The outputs of the equalizers are combined at chip level via a combiner which provides a single output. In another embodiment, a cluster multipath interference suppression (CMIS) circuit is incorporated into the receiver. The CMIS circuit includes a hard decision unit and a plurality of signal regeneration units to generate replicas of the multipath clusters. The replicas are subtracted from the respective outputs of the delay units and the results are input to the respective sliding window equalizers. In another embodiment, multiple antennas are used to receive and process the clusters.
Abstract:
Data estimation is performed in a wireless communication system using both oversampling and multiple reception antennas. A receive vector is produced for each antenna at a sampling interval which is a multiple of the chip rate of the received signal. A channel response matrix is produced for each antenna at a preferred multiple of the sampling rate. Each receive vector is processed using a sliding window based approach, where a plurality of successive windows are processed. For each window, a combined circulant channel response matrix is produced using the channel response matrices. Using the combined circulant channel response matrix and a combined received vector comprising each received vector in a discrete Fourier transform based approach to estimate a data vector corresponding to that window; and combining the data vector estimated in each window to form a combined data vector.
Abstract:
A receiver comprises a plurality of antenna elements for receiving a data signal. Each antenna element has a plurality of Rake fingers. Each Rake finger processes a received multipath component of the received data signal of its antenna element by applying a complex weight gain to that received multipath component. A complex weight gain generator determines the complex weight gain for each Rake finger for each antenna element using an input from all the Rake fingers. A summer combines an output of each Rake finger to produce an estimate of the data signal.
Abstract:
A receiver comprises a plurality of antenna elements for receiving a data signal. Each antenna element has a plurality of Rake fingers. Each Rake finger processes a received multipath component of the received data signal of its antenna element by applying a complex weight gain to that received multipath component. A complex weight gain generator determines the complex weight gain for each Rake finger for each antenna element using an input from all the Rake fingers. A summer combines an output of each Rake finger to produce an estimate of the data signal.
Abstract:
A channel estimation method which reduces the strain on resources of a Rake receiver using a complex weight gain (CWG) algorithm. In one embodiment, a non-adaptive algorithm is used to average blocks of pilot symbols from several slots. In another embodiment, an adaptive algorithm implements sliding window averaging or a recursive filter. Using a CWG algorithm reduces the memory and processor requirements of the Rake receiver.
Abstract:
The present invention has many aspects. One aspect of the invention is to perform equalization using a sliding window approach. A second aspect reuses information derived for each window for use by a subsequent window. A third aspect utilizes a discrete Fourier transform based approach for the equalization. A fourth aspect relates to handling oversampling of the received signals and channel responses. A fifth aspect relates to handling multiple reception antennas. A sixth embodiment relates to handling both oversampling and multiple reception antennas.
Abstract:
The present invention is related to a method and apparatus for estimating Doppler speed in wireless communication. The apparatus comprises a receiver, a sampler, a pilot/data removal unit, a phase change rate (PCR) measurement unit, a Doppler speed value calculating unit. The receiver receives signals, and the sampler samples the received signals. The pilot/data removal unit removes pilot or data information from the samples. The PCR measurement unit measures a PCR of the samples and the Doppler speed value calculating unit calculates a Doppler speed value based on the PCR. The Doppler speed value calculating unit is preferably a look-up table or a mapping functional unit.
Abstract:
A receiver or an integrated circuit (IC) incorporated therein includes a fast Fourier transform (FFT)-based (or hybrid FFT-based) sliding window block level equalizer (BLE) for generating equalized samples. The BLE includes a noise power estimator, first and second channel estimators, an FFT-based chip level equalizer (CLEQ) and a channel monitor unit. The noise power estimator generates a noise power estimate based on two diverse sample data streams. The channel estimators generate respective channel estimates based on the sample data streams. The channel monitor unit generates a first channel monitor signal including truncated channel estimate vectors based on the channel estimates, and a second channel monitor signal which indicates an approximate rate of change of the truncated channel estimate vectors. The FFT-based CLEQ generates the equalized samples based on the noise power estimate, one-block samples of the first and second sample data streams, the channel estimates and the monitor signals.