Channel estimation under asynchronous packet interference

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.

Wavelet-Based Identification of Linear Discrete-Time Systems

Graphs are commonly used in various applications such as online social networks (OSNs), E-commerce systems and social recommender systems. Random walk sampling is often used to conduct statistical estimation over such graphs. This paper develops an algorithmic framework to reduce the mean square error of such statistical estimation. Our algorithmic framework is inspired by that the mean square error can be decomposed into a sum of the bias and variance of the estimator. More specifically, we apply the bootstrapping technique to design a bias reduction algorithm. A new feature of this bias reduction algorithm is that it allows the variance to increase whenever the bias can be further reduced. The increased variance may lead to a large mean square error of the estimator. We use multiple parallel random walks to reduce this variance such that it can be reduced to arbitrarily small by deploying a sufficient number of random walks. Our algorithmic framework enables one to attain different trade-offs between the sample complexity (i.e., number of parallel random walks) and the mean square error of the statistical estimation. Also, the proposed bias reduction algorithm is generic and can be applied to optimize a large class of random walk sampling algorithms. To demonstrate the versatility of the framework, we apply it to optimize the Metropolis random walk and simple random walk sampling. Extensive experiments confirm the effectiveness and efficiency of our proposed algorithmic framework.

The development of an efficient ground sampling strategy is critical to assess uncertainties associated with moderate- or coarse-resolution remote-sensing products. This work presents a comparison of estimating spatial means from fine spatial resolution images using spatial random sampling (SRS), Block Kriging (BK), and Means of Surface with Nonhomogeneity (MSN) at 1 km2 spatial scale. Towards this goal, we focus on the sampling strategies for ground data measurements and provide an assessment of the MODIS LAI product validated by the spatial means estimated by the above-mentioned three methods. The results of this study indicate that: (1) for its effective stratification strategies and its criteria of minimum mean square estimation error, MSN demonstrates the lowest mean squared estimation error for estimating the means of stratified nonhomogeneous surface; (2) BK is efficient in estimating the means of homogeneous surfaces without bias and with minimum mean squared estimation errors. The MODIS LAI product is assessed using the means estimated by SRS, BK, and MSN based on Landsat 8 OLI and SPOT HRV fine-resolution LAI maps. For heterogeneous surfaces, MSN results in low RMSE and high accuracy of MODIS LAI product compared with BK and SRS, whereas for homogeneous surfaces, the statistical parameters outputted by these three methods are similar. These results reveal that MSN is an effective method for estimating the spatial means for heterogeneous surfaces. There are differences in the accuracies of MODIS LAI product assessed by these three methods.

The multistage Wiener filter (MWF) is an iterative Schur complement procedure for estimating a scalar or a vector of random variables in the linear minimum mean square error sense. When a complementary orthogonal projector is used as a blocking matrix, intermediate vectors in the development of the MWF have singular covariance matrices, and the rationale of the MWF is undermined. For this situation, we validate the structure of the MWF by showing that it can be developed as an iterative generalized Schur complement procedure which relies on the Moore-Penrose generalized inverse. The validated filter does not require any generalized inverse, and it can be implemented as usual by using scalar Wiener filters when estimating a scalar random variable. Next, we show, under a condition which is commonly met in practice, that the MWF is a least squares reduced rank estimate of the Wiener filter from the Krylov subspace associated with the Wiener-Hopf equation. The MWF is then compared with Brillinger's reduced rank minimum mean square error filter. The two filters differ in the subspaces from which signal estimation is performed and in the order in which projection onto the respective subspaces and Wiener estimation are performed. A forward recursion for the mean square estimation error in each stage of the MWF is provided, and the performance of the MWF is demonstrated by a numerical example.

In this paper, we investigate the optimal jamming power allocation strategy for cognitive MIMO radar, which can quickly learn the jamming power allocation of the jammer and adaptively change its transmission strategy according to the current environment. The problem is modeled as a two person zero sum Stackelberg game, where the jammer is the leader and the radar is the follower. Minimum mean square error (MMSE) of the target impulse response estimation is utilized as the utility function, where the radar tries to minimize the MMSE while the jammer aims to maximize it. Particle Swarm Optimization (PSO) algorithm is applied to the problem and the Stackelberg Equilibrium (SE) is obtained. Simulation results illustrate the effectiveness of the optimal jamming strategy.

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.

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