Abstract: Provided are an apparatus and method for eliminating multi-user interference in a codebook-based beamforming system. A transmitter for providing a service to multi-users in the codebook-based beamforming system includes a beamformer for generating beamformed user signals by multiplying transmit data of users, to whom the service is to be provided, by corresponding weighting factor vectors using feedback information; a null space generator for generating a null space matrix orthogonal to weighting factor vectors of other users; and a projector for projecting the beamformed user signals on the corresponding null space matrix and transmitting the resulting signals through a plurality of antennas. Because the multi-user signals can maintain orthogonality, the performance degradation caused by the multi-user interference can be prevented.

Abstract: Provided is a scheduling apparatus and method for allocating resources to individual users in a multiantenna system. An upper layer determines resources to allocate to respective users one a long term, and provides the ratio to a scheduler. Then the scheduler determines coefficients appropriate for the provided ratio. On the assumption that a statistical channel characteristic for a user is constant for a predetermined time, the scheduler determines a channel capacity average and a standard deviation for the user. The scheduler schedules resource allocation for individual users according to the channel capacity average and standard deviation values, which are the statistical characteristic values, and instantaneous channel capacity fed back for the individual users, together with the found coefficients.

Abstract: A user allocating apparatus and method in a multiple antenna mobile communication system supporting multi-user diversity is provided. Each user calculates effective Signal-to-Interference Noise Ratios (SINRs) and bit loading over every possible detection order and selects a detection order maximizing the number of channels with a minimum product of the minimum symbol distance of a modulation scheme and an effective SINR, thereby minimizing an average error probability. A base station receives detection orders from users and selects a user with a maximum detection order value. The base station then transmits data to the selected user using modulation schemes determined by the user for the transmit antennas.

Abstract: The present invention based on the LUT enables to perform a filtering at a low speed on a place where a high-speed process is necessary. Particularly, the LUT structure having a block structure is advantageous for filtering at a low speed, and for decreasing memory usage. The filter structure proposed by the present invention provides the equivalent performances to those of the traditional ones, yet conducts the filtering on an interpolation process requiring a high-speed process previously at a low speed and at a low power instead. Moreover, compared to the traditional LUT based filter, the present invention provides a new structure with a decreased memory demand. In short, the present invention provides a new filter structure that enables to implement an interpolation filter, a high-speed processor of a sending end in a wireless telecommunication system, with a low complexity and a low power consumption.

Abstract: Disclosed herein is a scheduling method and apparatus based on eigen values robust against spatial correlation in a mobile communication system supporting multiple users by use of multiple transmit/receive antennas. A user terminal estimates channel characteristics from the transmit antennas to the user terminal, decomposes the estimated channel characteristics into receive channel characteristics and transmit channel characteristics, and feeds back a transmit principal eigen vector to the base station (BS). The BS selects a predetermined number of user terminals having the least correlation using the feedback principal eigen vectors from a plurality of user terminals, constructs a user precoding matrix by combining the principal eigen vectors of the selected user terminals, multiplies data streams for the selected user terminals by the user precoding matrix and transmits the multiplied data to the selected user terminals.

Abstract: A method and apparatus for determining a shuffling pattern in a DSTTD. In the apparatus, a channel estimator estimates channel characteristics from a plurality of transmit antennas to a plurality of receive antennas. A shuffling pattern selector calculates a minimum receive SNR for each of all available shuffling patterns, and selects a shuffling pattern having the largest minimum receive SNR as the optimum shuffling pattern. This efficient shuffling pattern selection directly improves BER performance at a receiver.

Abstract: An apparatus and method for performing channel estimation in an OFDM (Orthogonal Frequency Division Multiplexing) system using multiple antennas, and a method for performing channel estimation using received sub-carriers in an OFDM system for receiving a sub-carrier having a training symbol and another sub-carrier having no training symbol comprising the steps of: a) differently setting a weight associated with a reliability of the sub-carrier having the training symbol and a weight associated with a reliability of the sub-carrier having no training symbol, b) measuring channel estimation errors associated with individual received sub-carriers, and c) performing channel estimation using the measured channel estimation errors and the set weights.

Abstract: A method for filtering signals from a nonlinear dynamical system is provided. An initial enhanced point is set to a noisy point obtained from the signals. An intermediate enhanced point is estimated. A new enhanced point is calculated using the estimated point and a weighting constant. Estimation of an intermediate enhanced point and calculation of a new enhanced point are iterated until the computed point converges to a true enhanced point which represents a noise reduced signal to provide filtered signals, wherein the new enhanced point is computed by the equation {circumflex over (x)}n(i)={circumflex over (x)}n(i−1)+K3[{circumflex over (x)}n(i,temp)−{circumflex over (x)}n(i−1)] wherein {circumflex over (x)}n(i) is the new enhanced point, {circumflex over (x)}n(i-temp) is the intermediate enhanced point for iteration i, {circumflex over (x)}n(i−1) is the enhanced point at iteration i−1, and K3 is a weighing constant.

Abstract: The present invention based on the LUT enables to perform a filtering at a low speed on a place where a high-speed process is necessary. Particularly, the LUT structure having a block structure is advantageous for filtering at a low speed, and for decreasing memory usage. The filter structure proposed by the present invention provides the equivalent performances to those of the traditional ones, yet conducts the filtering on an interpolation process requiring a high-speed process previously at a low speed and at a low power instead. Moreover, compared to the traditional LUT based filter, the present invention provides a new structure with a decreased memory demand. In short, the present invention provides a new filter structure that enables to implement an interpolation filter, a high-speed processor of a sending end in a wireless telecommunication system, with a low complexity and a low power consumption.

Abstract: A method for filtering signals from a nonlinear dynamical system is provided. An initial enhanced point is set to a noisy point obtained from the signals. An intermediate enhanced point is estimated. A new enhanced point is calculated using the estimated point and a weighting constant.

Abstract: Systems and methods for generalized iterative noise reduction for contaminated chaotic signals which are simple and easily implemented are disclosed. One system and method estimates the deviation of the observed signal from the nearest noise-free signal and uses the result to get a noise-reduced signal. Another system and method enhances the observed signal by iteratively seeking to minimize the signal according to a cost function.