Dual-lattice ordering and partial lattice reduction for SIC-based MIMO detection

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we propose low-complexity lattice detection algorithms for successive interference cancelation (SIC) in multi-input multi-output (MIMO) communications. First, we present a dual-lattice view of the vertical Bell Labs Layered Space-Time (V-BLAST) detection. We show that V-BLAST ordering is equivalent to applying sorted QR decomposition to the dual basis, or equivalently, applying sorted Cholesky decomposition to the associated Gram matrix. This new view results in lower detection complexity and allows simultaneous ordering and detection. Second, we propose a partial reduction algorithm that only performs lattice reduction for the last several, weak substreams, whose implementation is also facilitated by the dual-lattice view. By tuning the block size of the partial reduction (hence the complexity), it can achieve a variable diversity order, hence offering a graceful tradeoff between performance and complexity for SIC-based MIMO detection. Numerical results are presented to compare the computational costs and to verify the achieved diversity order

Computation of the para-pseudoinverse for oversampled filter banks: Forward and backward Greville formulas

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Frames and oversampled filter banks have been extensively studied over the past few years due to their increased design freedom and improved error resilience. In frame expansions, the least square signal reconstruction operator is called the dual frame, which can be obtained by choosing the synthesis filter bank as the para-pseudoinverse of the analysis bank. In this paper, we study the computation of the dual frame by exploiting the Greville formula, which was originally derived in 1960 to compute the pseudoinverse of a matrix when a new row is appended. Here, we first develop the backward Greville formula to handle the case of row deletion. Based on the forward Greville formula, we then study the computation of para-pseudoinverse for extended filter banks and Laplacian pyramids. Through the backward Greville formula, we investigate the frame-based error resilient transmission over erasure channels. The necessary and sufficient condition for an oversampled filter bank to be robust to one erasure channel is derived. A postfiltering structure is also presented to implement the para-pseudoinverse when the transform coefficients in one subband are completely lost

Hardware co-processor to enable MIMO in next generation wireless networks

One prevailing technology in wireless communication is Multiple Input, Multiple Output (MIMO) communication. MIMO communication simultaneously transmits several data streams, each from their own antenna within the same frequency channel. This technique can increase data bandwidth by up to a factor of the number of transmitting antennas, but comes with the cost of a much higher computational complexity for the wireless receiver. MIMO communication exploits differing channel effects caused by physical distances between antennas to differentiate between transmitting antennas, an intrinsically two dimensional operation. Current Digital Signal Processors (DSPs), on the other hand, are designed to perform computations on one dimensional vectors of incoming data. To compensate for the lack of native support of these higher dimensional operations, current base stations are forced to add multiple new processing elements while many mobile devices cannot support MIMO communication. In order to allow wireless clients and stations to have native support of the two dimensional operations required by MIMO communication, a hardware co-processor was designed to allow the DSP to offload these operations onto another processor to reduce computation time

Efficient and Stable Algorithms to Extend Greville's Method to Partitioned Matrices Based on Inverse Cholesky Factorization

Greville's method has been utilized in (Broad Learn-ing System) BLS to propose an effective and efficient incremental learning system without retraining the whole network from the beginning. For a column-partitioned matrix where the second part consists of p columns, Greville's method requires p iterations to compute the pseudoinverse of the whole matrix from the pseudoinverse of the first part. The incremental algorithms in BLS extend Greville's method to compute the pseudoinverse of the whole matrix from the pseudoinverse of the first part by just 1 iteration, which have neglected some possible cases, and need further improvements in efficiency and numerical stability. In this paper, we propose an efficient and numerical stable algorithm from Greville's method, to compute the pseudoinverse of the whole matrix from the pseudoinverse of the first part by just 1 iteration, where all possible cases are considered, and the recently proposed inverse Cholesky factorization can be applied to further reduce the computational complexity. Finally, we give the whole algorithm for column-partitioned matrices in BLS. On the other hand, we also give the proposed algorithm for row-partitioned matrices in BLS

Multi-axis transient vibration testing of space objects: Test philosophy, test facility, and control strategy

IABG has been using various servohydraulic test facilities for many years for the reproduction of service loads and environmental loads on all kinds of test objects. For more than 15 years, a multi-axis vibration test facility has been under service, originally designed for earthquake simulation but being upgraded to the demands of space testing. First tests with the DFS/STM showed good reproduction accuracy and demonstrated the feasibility of transient vibration testing of space objects on a multi-axis hydraulic shaker. An approach to structural qualification is possible by using this test philosophy. It will be outlined and its obvious advantages over the state-of-the-art single-axis test will be demonstrated by example results. The new test technique has some special requirements to the test facility exceeding those of earthquake testing. Most important is the high reproduction accuracy demanded for a sophisticated control system. The state-of-the-art approach of analog closed-loop control circuits for each actuator combined with a static decoupling network and an off-line iterative waveform control is not able to meet all the demands. Therefore, the future over-all control system is implemented as hierarchical full digital closed-loop system on a highly parallel transputer network. The innermost layer is the digital actuator controller, the second one is the MDOF-control of the table movement. The outermost layer would be the off-line iterative waveform control, which is dedicated only to deal with the interaction of test table and test object or non-linear effects. The outline of the system will be presented

Two Ridge Solutions for the Incremental Broad Learning System on Added Nodes

The original Broad Learning System (BLS) on new added nodes and its existing efficient implementation both assume the ridge parameter is near 0 in the ridge inverse to approximate the generalized inverse, and compute the generalized inverse solution for the output weights. In this paper, we propose two ridge solutions for the output weights in the BLS on added nodes, where the ridge parameter can be any positive real number. One of the proposed ridge solutions computes the output weights from the inverse Cholesky factor, which is updated by extending the existing inverse Cholesky factorization. The other proposed ridge solution computes the output weights from the ridge inverse, and updates the ridge inverse by extending the Greville method that can only computes the generalized inverse of a partitioned matrix. The proposed BLS algorithm based on the ridge inverse requires the same complexity as the original BLS algorithm, while the proposed BLS algorithm based on the inverse Cholesky factor requires less complexity and training time than the original BLS and the existing efficient BLS. Both the proposed ridge solutions for BLS achieve the same testing accuracy as the standard ridge solution in the numerical experiments. The difference between the testing accuracy of the proposed ridge solutions and that of the existing generalized inverse solutions is negligible when the ridge parameter is very small, and becomes too big to be ignored when the ridge parameter is not very small. When the ridge parameter is not near 0, usually the proposed two ridge solutions for BLS achieve better testing accuracy than the existing generalized inverse solutions for BLS, and then the former are more preferred than the latter

On adaptive transmission, signal detection and channel estimation for multiple antenna systems

This research concerns analysis of system capacity, development of adaptive transmission schemes with known channel state information at the transmitter (CSIT) and design of new signal detection and channel estimation schemes with low complexity in some multiple antenna systems. We first analyze the sum-rate capacity of the downlink of a cellular system with multiple transmit antennas and multiple receive antennas assuming perfect CSIT. We evaluate the ergodic sum-rate capacity and show how the sum-rate capacity increases as the number of users and the number of receive antennas increases. We develop upper and lower bounds on the sum-rate capacity and study various adaptive MIMO schemes to achieve, or approach, the sum-rate capacity. Next, we study the minimum outage probability transmission schemes in a multiple-input-single-output (MISO) flat fading channel assuming partial CSIT. Considering two special cases: the mean feedback and the covariance feedback, we derive the optimum spatial transmission directions and show that the associated optimum power allocation scheme, which minimizes the outage probability, is closely related to the target rate and the accuracy of the CSIT. Since CSIT is obtained at the cost of feedback bandwidth, we also consider optimal allocation of bandwidth between the data channel and the feedback channel in order to maximize the average throughput of the data channel in MISO, flat fading, frequency division duplex (FDD) systems. We show that beamforming based on feedback CSI can achieve an average rate larger than the capacity without CSIT under a wide range of mobility conditions. We next study a SAGE-aided List-BLAST detection scheme for MIMO systems which can achieve performance close to that of the maximum-likelihood detector with low complexity. Finally, we apply the EM and SAGE algorithms in channel estimation for OFDM systems with multiple transmit antennas and compare them with a recently proposed least-squares based estimation algorithm. The EM and SAGE algorithms partition the problem of estimating a multi-input channel into independent channel estimation for each transmit-receive antenna pair, therefore avoiding the matrix inversion encountered in the joint least-squares estimation

Reduced complexity detection for massive MIMO-OFDM wireless communication systems

PhD ThesisThe aim of this thesis is to analyze the uplink massive multiple-input multipleoutput with orthogonal frequency-division multiplexing (MIMO-OFDM) communication systems and to design a receiver that has improved performance with reduced complexity. First, a novel receiver is proposed for coded massive MIMO-OFDM systems utilizing log-likelihood ratios (LLRs) derived from complex ratio distributions to model the approximate effective noise (AEN) probability density function (PDF) at the output of a zero-forcing equalizer (ZFE). These LLRs are subsequently used to improve the performance of the decoding of low-density parity-check (LDPC) codes and turbo codes. The Neumann large matrix approximation is employed to simplify the matrix inversion in deriving the PDF. To verify the PDF of the AEN, Monte-Carlo simulations are used to demonstrate the close-match fitting between the derived PDF and the experimentally obtained histogram of the noise in addition to the statistical tests and the independence verification. In addition, complexity analysis of the LLR obtained using the newly derived noise PDF is considered. The derived LLR can be time consuming when the number of receive antennas is very large in massive MIMO-OFDM systems. Thus, a reduced complexity approximation is introduced to this LLR using Newton’s interpolation with different orders and the results are compared to exact simulations. Further simulation results over time-flat frequency selective multipath fading channels demonstrated improved performance over equivalent systems using the Gaussian approximation for the PDF of the noise. By utilizing the PDF of the AEN, the PDF of the signal-to-noise ratio (SNR) is obtained. Then, the outage probability, the closed-form capacity and three approximate expressions for the channel capacity are derived based on that PDF. The system performance is further investigated by exploiting the PDF of the AEN to derive the bit error rate (BER) for the massive MIMO-OFDM system with different M-ary modulations. Then, the pairwise error probability (PEP) is derived to obtain the upper-bounds for the convolutionally coded and turbo coded massive MIMO-OFDM systems for different code generators and receive antennas. Furthermore, the effect of the fixed point data representation on the performance of the massive MIMO-OFDM systems is investigated using reduced detection implementations for MIMO detectors. The motivation for the fixed point analysis is the need for a reduced complexity detector to be implemented as an optimum massive MIMO detector with low precision. Different decomposition schemes are used to build the linear detector based on the IEEE 754 standard in addition to a user-defined precision for selected detectors. Simulations are used to demonstrate the behaviour of several matrix inversion schemes under reduced bit resolution. The numerical results demonstrate improved performance when using QR-factorization and pivoted LDLT decomposition schemes at reduced precision.Iraqi Government and the Iraqi Ministry of Higher Education and Scientific researc