In this paper, we use the maximum likelihood principle and the data filtering technique to study the identification issue of the multivariate equation-error system whose outputs are contaminated by an ARMA noise process. The key is to break the system into several regressive identification subsystems based on the number of the outputs. Then a multivariate equation-error subsystem is transformed into a filtered model and a filtered noise model, and a filtering based maximum likelihood extended stochastic gradient algorithm is derived to estimate the parameters of these two models. The filtering based maximum likelihood extended stochastic gradient algorithm has higher parameter estimation accuracy than the maximum likelihood generalized extended stochastic gradient algorithm and the maximum likelihood recursive generalized extended least squares algorithm. The simulation examples indicate that the proposed methods work well.

Data filtering based maximum likelihood gradient estimation algorithms for a multivariate equation-error system with ARMA noise

This article provides an efficient hardware generator for high-performance sets-of-real-numbers (SORNs) arithmetic. Complex datapaths are automatically built in VHDL, comprising the generation of arithmetic operations and functions as well as all necessary interconnects. Various SORN datatypes can be easily set up enabling high adaptivity to different applications. For further performance improvements additional optimization and implementation techniques are considered as well. For evaluation, a SORN-based symbol detector for a multiantenna wireless communication scenario is generated that solves a system of linear equations. As SORN-based signal processing allows a quick but rough calculation of the system equations outcome, it can be exploited to reject possible input vectors that do not satisfy the general constraints of the signal detection task. Hence, this generally leads to a heavily reduced set of remaining solutions. Multiple hardware architectures with different SORN datatypes are generated and compared. Further, an analysis is performed considering the signal-to-noise ratio (SNR). Finally, logic synthesis is applied to selected designs and compared to references from the literature highlighting this article to be highly hardware-efficient and suitable for application-specific signal processing.

A Hardware Generator for SORN Arithmetic

Reliability feedback–aided low-complexity detection in uplink massive MIMO systems

Hardware and timing complexity of Sphere Decoding (SD) increases exponentially with antenna and modulation order. Hardware complexity can be optimized by proper choice of the data width of different variables associated with the detection process and two's complement based arithmetic is conventionally used. In this work, we have proposed a 1's complement based approximation to further simplify the computational overhead. The proposed technique can reduce 14% and 11% arithmetic operations for $4\times 4$ and $6 \times 6$ MIMO systems respectively. As a case study, a $4 \times 4$ MIMO-OFDM system with 16-QAM modulation is simulated and performance of proposed method is evaluated.

Performance Evaluation of Fixed Point FSD Using 1's Complement Based Approximation

All-Pass Filters as a Low-Complexity PAPR Reduction Scheme for SC-FDMA System

The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm has variable complexity, and its complexity increases rapidly with decreasing signal-to-noise ratio (SNR). In this paper, we propose a novel ML detection algorithm for the MIMO system based on differential metrics. We define the differential metrics and derive the associated recursive calculation. We then give the indicative functions, which can be used to possibly find some ML-detected bits of the initial sequence. The indicative functions are further applied to implement an efficient tree search for ML detection. The proposed algorithm does not need QR decomposition and matrix inversion. The tree search process needs only the additive operation, while the number of multiplications before the tree search is constant. Our algorithm can achieve the exact ML detection as the SD algorithm. Unlike the SD algorithm, the complexity of our algorithm reduces with decreasing SNR, whereas at high SNR, the complexity is nearly constant. We also give the convergence analysis for the SD and proposed algorithms, and the simulation verifies our analysis. For the proposed algorithm, the number of necessary memory is constant during the tree search, and the implementation by parallel processing is possible. The soft output of ML-detected bits can also be generated in our algorithm.

Maximum-Likelihood Detection for MIMO Systems Based on Differential Metrics

The multiple-input multiple-output (MIMO) technology can make full use of spectrum and increase the communication throughput. In the coded MIMO system, the main challenge of soft detection is to efficiently generate the loglikelihood ratios (LLR) values for channel decoder. The exact maximum a posteriori (MAP) probability detection can guarantee the optimal performance, but its realization is difficult due to its enormous complexity. In this paper, we propose efficient soft detection algorithms based on differential metrics. We apply the differential metrics for the list sphere decoding, and propose the list gradient algorithm. We further propose a novel algorithm that can generate the values of LLR and provide a trade-off between performance and complexity. The proposed algorithms do not need the QR decomposition and matrix inversion. The proposed algorithms have fixed complexity, and are appropriate for pipelined hardware implementation. The numerical results verify the efficiency of our algorithms.

Wang-Yueh Chang

Papers

Maximum-Likelihood Detection for MIMO Systems Based on Differential Metrics

Efficient Soft MIMO Detection Algorithms Based on Differential Metrics