Wavelet-Based Identification of Linear Discrete-Time Systems

This paper investigates the placement of training symbols within the data packets of a wireless system in which transmissions are subject to asynchronous interference. The minimum mean square error of the training-based channel estimator is expressed as a function of the Fisher information of the received signal. It is shown that the placement that minimizes the minimum mean square error should be searched for within a set containing half as many elements as the number of training symbols in the packet. Furthermore, a lower bound on the minimum mean square error is derived and analyzed. It is shown that this bound is tight when the power of the interference is high. The placement of the training symbols in two clusters of equal or quasiequal length at the two edges of the data packet minimizes the lower bound for all values of the parameters and, thus, gives the solution of the problem for high values of the interference power. The influence of the training symbols placement on the data transmission performance is also investigated.

In time division-synchronous code division multiple access systems, the channel estimation for multiple subscribers requires the computation of very complicated algorithms through short training sequences. This situation causes mismodeling of the actual channels and introduces significant errors in the detected data of multiple users. This paper presents a novel channel estimation method with low complexity, which relies on reducing the rank order of the total channel matrix H. We exploit the rank deficient of H to reduce the number of parameters that characterizes this matrix. The adopted reduced rank technique is based on singular value decomposition algorithm. Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. Simulation results of the normalized mean square error for the above mentioned estimators showed the superiority of reduced rank estimators. Multi-user joint data detectors based linear equalizers are used to suppress inter-symbol interference and mitigate intra-cell multiple access interference. The detectors: zero forcing block linear equalizer and minimum mean square error block linear equalizer algorithms are considered in this paper to recover the data. The results of bit error rate simulation have shown that reduced rank-JCE based detectors have an improvement by 5 dB lower than other traditional full rank-JCE based detectors.

We generalize the multiuser (CDMA spread spectrum) communication systems to the fading environments. We first extend Verdu's (1986) conventional optimum receiver to Rayleigh-fading environments and then evaluate its performance. Having no knowledge of the received power at the receiving end, we therefore need an estimator to efficiently estimate the received signal strength of each user in fading environments. A linear minimum mean square error (LMMSE) unbiased estimator is proposed to attain this goal. By using the minimum mean square error (MMSE) Bayesian estimation, we further propose the LMMSE bit estimator for efficient demodulation. Its performance is close to the optimum multiuser receiver but with a much simpler polynomial complexity. To further reduce the complexity, we extend the LMMSE estimator to the sequential LMMSE estimator. In sequential estimation, we do not need to implement the matched filter banks and to perform the matrix inversion when estimating. In addition, it converges after approximately 2k iterations, where k is the number of users. With this fast convergence property and the simple structure, the sequential LMMSE estimator provides an attractive alternative to the implementation of a multiuser system.

In present days, there has been a race among the communication systems in different schemes and generations. Gradually the transmission rate of data is increasing tremendously and breaking the previous generation's record. Now people are thinking of transmitting data beyond imagination. Using multiple antennas both at transmitter and receiver side, MIMO wireless technology efficiently provides very high spectral efficiency, boost the data transmission rate, enhance link reliability and improve system coverage. While transmitting data over mobile radio channels at high bit rates, the impulse response of the channel can be extended over a lot of symbol periods, leading fading and Inter-symbol interference (lSI). Equalizers are used to minimize Fading and interference effects. The analysis of the paper is studying the performance of Minimum mean square error method in terms of BER for MIMO (2×2) and (3×3) wireless channels. Binary Phase Shift Keying (BPSK) modulation technique was used in Rayleigh flat fading channel. Simulating in MATLAB results show that Minimum mean square error (MMSE) receivers is good for noise free channel and is successful in removing ISI. Successive interference method with optimal ordering outperforms the MMSE however a few problems arise because of iterative nature of the algorithm.

In single-carrier aeronautical communication systems, the time-varying multipath propagation characteristics of the channel give rise to severe inter-code interference. Radio channel distortion compensation and inter-code interference cancellation are generally achieved by equalization techniques. In this paper, zero-forcing equalization, minimum mean square error equalization and minimum mean square error adaptive equalization algorithms for SC-FDE systems are investigated, but the traditional LMS algorithms cannot meet the performance requirements of convergence speed and steady-state error at the same time because the step size factor is fixed. This paper investigates the variable step size LMS algorithm and proposes an improved variable step size LMS algorithm, which uses the non-linear relationship between the error factor and the error function to construct a correlation correction function, to regulate the error factor in a small range, and to further correct the step size factor through the change of the error factor. The LMS algorithm is divided into a training mode and a tracking mode, and the two modes are combined to achieve a faster convergence rate. The computer simulation results are consistent with the theoretical analysis and confirm that the algorithm outperforms the original algorithm.

In TDD-CDMA, the detection method conventionally is MMSE-BDFE (minimum mean square error-block decision feedback equalization) based joint detection. MMSE-IC is an efficient detection algorithm in V-BLAST. To adopt the advantages of MMSE-IC (minimum mean square error-interference cancellation), we can embed MMSE-BDFE into MMSE-IC. Furthermore, we can postpone the interference cancellation after decoding, and joint detect the remained users' data from the remained signals in the next iteration. By the aid of interference cancellation after decoding, the decoding decision gain and interference cancellation gain can be obtained, so the performance is improved especially when the users' number is large. The experiments show that the proposed methods have better performance.

A generalized estimator representing a class of estimators for the estimation of ratio and product of population parameters has been proposed. A sub­class of optimum estimators from the proposed class has been investigated and it has been shown that every member of the sub­class has the same minimum mean square error. Further the optimum value (depending upon population parameters) when replaced from sample values gives the estimators having the minimum mean square errors of the optimum estimators.

This paper introduces four block joint detection (JD) receivers which combining the orthogonal frequency division multiplexing (OFDM) and code division multiple access (CDMA) techniques, i.e., OFDM-CDMA receiver. The performance of the OFDM-CDMA receiver with orthogonal and non-orthogonal spreading sequences is investigated in mobile radio channels. The sequences considered as spreading codes are orthogonal Walsh-Hadamard, Orthogonal Gold, Gold and maximim length sequences or m - sequences. The JD algorithms for the OFDM- CDMA receiver are Zero Forcing Block Linear Equalizer (ZF- BLE), Minimum Mean Square Error Block Linear Equalizer (MMSE-BLE), with and without decision feedback (DF). Time- domain equalization does not require DFT or FFT operations and can be used to compensate the channel fading effect. Hence for hardware implementation, the time domain interpretation can lead to a more efficient structure when compared with the frequency domain approach. The bit error rate (BER) performance comparisons of different spreading codes and equalizers are shown in the simulations.

Multiple user satellites monitor is an important technology which can improve both the monitoring network coverage and the capacity of multi-target signal processing. Multi user detection (MUD) is one of the key technology for multiple user satellites communication. In this paper, a new algorithm for MUD is implemented on field-programmable gate array (FPGA). This FPGA design is based on the minimum mean square error (MMSE) algorithm which consists of two main components: Matched filter and minimum mean square error matrix. Additionally, in order to conduct Monte Carlo simulation, we adopt a way which based on Mat lab, UART and FPGA platform.

Summary Satellite communication systems will be a key component of 5G and 6G networks to achieve the goal of providing unlimited and ubiquitous communications and deploying smart and sustainable networks. To meet the ever‐increasing demand for higher throughput in 5G and beyond, aggressive frequency reuse schemes (i.e., full frequency reuse), combined with digital beamforming techniques to cope with the massive co‐channel interference, are recognized as a key solution. Aimed at (i) eliminating the joint optimization problem among the beamforming vectors of all users, (ii) splitting it into distinct ones, and (iii) finding a closed‐form solution, we propose a beamforming algorithm based on maximizing the users' signal‐to‐leakage‐and‐noise ratio served by a low Earth orbit satellite. We investigate and assess the performance of several beamforming algorithms, including both those based on channel state information at the transmitter, that is, minimum mean square error and zero forcing, and those only requiring the users' locations, that is, switchable multi‐beam. Through a detailed numerical analysis, we provide a thorough comparison of the performance in terms of per‐user achievable spectral efficiency of the aforementioned beamforming schemes, and we show that the proposed signal to‐leakage‐plus‐noise ratio beamforming technique is able to outperform both minimum mean square error and multi‐beam schemes in the presented satellite communication scenario.

Image intensity matching technique: Application to the environmental scanning electron microscope

In this paper, a minimum mean square error-optimized mixture-based estimator is used for packet video error concealment. At the same time as on-line computational complexity is reduced, performance in peak signal-to-noise ratio (PSNR) is increased in comparison with a Gaussian mixture-based estimator that obtains its parameters through probability density estimation by means of the expectation maximization algorithm. Moreover, our method increases performance in PSNR compared to several other previous error concealment algorithms.

The multikernel adaptive filters based on the minimum mean square error (MMSE) criterion have been proposed to improve the performance of the kernel least mean square (KLMS), efficiently. However, these multikernel methods suffer from large computational burden as well as instability in impulsive noises. To address these issues, a novel multikernel method is proposed by replacing the trace operation of a matrix with the inner product of two vectors, thus leading to higher computational efficiency, significantly. Based on the maximum correntropy criterion, the multikernel maximum correntropy (MKMC) algorithm is therefore proposed. To further reduce the complexity, an online vector quantization strategy is presented for MKMC to generate the quantized MKMC (QMKMC) algorithm. Monte Carlo simulations on different nonlinear examples validate the superiorities of the proposed two algorithms.

In wireless sensor networks (WSN), measurements are always corrupted by outliers or impulsive noise. Cubature information filtering (CIF) is founded based on minimum mean square error (MMSE) criterion, which is not applicable to non-Gaussian noise. Hence, a novel robust CIF (RCIF) is derived based on maximum correntropy criterion (MCC) to enhance the robustness of state estimation in the local node. For the information fusion, weighted average consensus (WAC) based distributed RCIF (DRCIF) is founded to improve the stability of sensor networks and the accuracy of state estimation. The estimation error of DRCIF is proved to be bounded in mean square. Numerical simulations are provided to evaluate the effectiveness of proposed algorithms.

Nonlinear system identification has been studied under the assumption that the noise has finite second and higher order statistics. In many practical applications, impulsive measurement noise severely weakens the effectiveness of conventional methods. In this paper, /spl alpha/-stable noise is used as a noise model. In such case, the minimum mean square error (MMSE) criterion is no longer an appropriate metric for estimation error due to the lack of finite second-order statistics of the noise. Therefore, we adopt minimum dispersion criterion, which in turn leads to the adaptive least mean pth power (LMP) algorithm. It is shown that the LMP algorithm under the /spl alpha/-stable noise model converges as long as the step size satisfies certain conditions. The effect of p on the performance is also investigated. Compared with conventional methods, the proposed method is more robust to impulsive noise and has better performance.