Mitsubishi Electric Research Laboratories, Inc.

Man-On Pun

Zafer Sahinoglu

Pu Wang

Philip Orlik

Jinyun Zhang

Ron Porat

Koon Hoo Teo

Toshiaki Koike-Akino

Chunjie Duan

Neelesh B Mehta

Zhifeng Tao

Raymond Yim

Ramesh Annavajjala

Dirk Brinkman

Gennadiy V Vinokur

Tiangao Gou

The embodiments of the invention provide a method for selecting antennas for date transmission in a wireless communication network including user equipment (UE). The network is assigned a band of frequencies, wherein the band is partitioned into at least one set of subbands of the band according to a sounding reference signal (SRS) bandwidth configuration in a form of a codetree having a plurality levels and each level is associated with a partition coefficient. The UE is configured to transmit frequency-hopped SRS on the set of subbands using subsets of the set of antennas. First, the method determines if a number of subbands in the set of the subbands is odd or even based on the SRS bandwidth configuration, and selects a particular subset of the antennas according to whether the number is odd or even. Then, the SRS is transmitted from the particular subset of the antennas.

Antenna selection with frequency-hopped sounding reference signals

A method for detecting a target in a non-homogeneous environment using a space-time adaptive processing of a radar signal includes normalizing training data of the non-homogeneous environment to produce normalized training data; determining a normalized sample covariance matrix representing the normalized training data; tracking a subspace represented by the normalized sample covariance matrix to produce a clutter subspace matrix; determining a test statistic representing a likelihood of a presence of the target in the radar signal based on the clutter subspace matrix and a steering vector; and comparing the test statistic with a threshold to detect the target.

Method for detecting targets using space-time adaptive processing

This invention provides a method for exploiting precoder optimization gains and multi-user diversity gains with interference alignment in general MIMO wireless networks including multiple users. Specifically, two embodiments exploit either a gradient-based search or iteratively orthogonalizing inference. The method can achieve near-optimal performance at a low complexity. Furthermore, a scheduling criterion is provided for wireless networks comprised of a large number of mobile stations in each cell. The criterion can be done independently in each cell to significantly reduce information exchanged between base stations in different cells compared to the methods that perform joint scheduling over all cells. The two embodiments can be utilized in a spectrally efficient communications network equipped with relaying nodes.

Method for reducing interference in multi-cell multi-user wireless networks

A blanking scheme for mitigating impulsive noise in wireless networks is based on the signal-to-noise ratio (SNR) of symbols. To fully gain the benefits of the SNR-based blanking scheme, two methods are developed, namely a multi-level thresholding scheme in the time-, spatial- and frequency-domains, and a weighted-input error-correction decoding. The symbols are conditioned as a function of the estimated SNR in time-, frequency-, or spatial-domains or combinations therefore, and the conditioning is applied to an amplitude, phase, or energy level, or combinations thereof.

SNR-based blanking scheme for impulsive noise mitigation in wireless networks

Power quality (PQ) events in a waveform in a power distribution network are detected by sampling the waveform. For each sample, a normalized sample is determined by subtracting an undistorted waveform from the sample. An accumulated weighted log-likelihood ratio (LLR) is determined for all normalized samples, as well as a minimum of the accumulated weighted LLR. If the difference between the accumulated weighted LLR an the minimum LLR is greater than a predetermined threshold, then the PQ event is signaled. The network can be a smart grid.

Detecting power quality events in power distribution networks

A method provides space-time adaptive processing (STAP) for target detection using adaptive matched filters (AMF). A generalized likelihood ratio test (GLRT) is determined where spatial and temporal correlation matrices Q and A are assumed. Then, the correlation matrices A and Q are replaced with maximum likelihood (ML) estimates obtained only from training signals subject to a persymmetric constraint.

Persymmetric parametric adaptive matched filters for detecting targets using space-time adaptive processing of radar signals

Channel state information for closed-loop transmit precoding in MIMO networks is fed back from the MSs to the BSs. The feedback is quantized using codebooks shared by the MSs and BSs to reduce overhead. The codebooks can be full-rank or rank-one. The quantized feedback is applicable to any definitions of MIMO channel covariance matrix as well as MIMO channel matrix. Since these codebooks are designed for closed-loop MIMO precoded transmissions, no additional memory is needed to store the codebooks at the BS and the MS only for the quantized feedback purposes.

Feedback for transmit precoding in wireless networks

Channel state information in a closed-loop, multiple-input, multiple-output wireless networks is fed back from each mobile station to a base station by first determining a transmit covariance matrix R, and applying a singular value decomposition (SVD) R=U&#x3a3;V, where U, V are left and right singular vector matrices, &#x3a3; is a diagonal matrix with singular values. The matrix V includes column vectors V. A beamforming vector v=[1 exp(j&#x3a6;)exp(j2&#x3a6;) . . . exp(j&#x3a6;)]/&#x221a;{square root over (N)}] is approximated by the column vector V having a maximum magnitude, where &#x3a6; is a real number. Then, only the angle &#x3a6; is fed back using a phase modulation mapping of the components exp(j&#x3a6;) onto the associated subcarrier.

Parametric compression of rank-1 analog feedback

A wireless network master node periodically broadcasts beacons that specify a structure of a following fixed length superframe. Slave nodes determine a channel condition between each slave and the master. Then, the set of slaves is partitioned into subsets of slaves according to the channel conditions. The master assigns, to each slave, a transmission rate in a low to high order according to the channel conditions, and the slaves transmit data to the master in the low to high order between two consecutive beacons, wherein the subsets of slaves with a higher transmission rate also receive the data from the subsets of slaves with a lower transmission rate, and wherein a slave with a higher transmission rate includes a part of or all the data from a slave with a lower transmission rate.

Method and network for transmitting data in a wireless network with fixed transmission intervals

A method surpresses clutter in a space-time adaptive processing system. The method achieves low-complexity computation via two steps. First, the method utilizes an improved fast approximated power iteration method to compress the data into a much smaller subspace. To further reduce the computational complexity, a progressive singular value decomposition (SVD) approach is employed to update the inverse of the covariance matrix of the compressed data. As a result, the proposed low-complexity STAP procedure can achieve near-optimal performance with order-of-magnitude computational complexity reduction as compared to the conventional STAP procedure.

Method for suppressing clutter in space-time adaptive processing systems

A method detects a target in a radar signal using space-time adaptive processing. A test statistic is where xis a test signal, xare K training signals, &#x3b1; is an unknown amplitude of a target signal within the test signal, &#x3bb; is a scaling factor, R is a covariance matrix of the training signals, and a function max returns a maximum values. The test statistic is compared to a threshold to determine whether the target is present, or not.

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Man-On Pun