Length Of Data Vector Research Articles

Partially Loaded Superimposed Training Scheme for Large MIMO Uplink Systems

This paper proposes a new superimposed training (ST) scheme for uplink multi-user multi-cell system, where each base station, equipped with a large number of antennas (M), communicates to single antenna users. In uplink training phase, large number of users within limited coherence time introduces the pilot contamination, which causes two types of interferences in data estimation. The first type, which is referred as self interference, arises due to the dependence between channel estimate and estimation error of the same user, while the second type, known as cross interference, occurs because of the correlation between ST vectors of different users. In this paper, an ST scheme with variable data length is proposed for Rician fading channels. For simplicity of analysis, a single cell model is considered first to derive mean squared error and signal to interference plus noise ratio. The analysis is further extended to multi-cell system. Various limiting cases are investigated, and the design parameters viz., power allocation factor and length of data vector, are optimized. Simulation results verify that the proposed ST scheme reduces self interference, and yields sum rate improvement over conventional ST scheme.

Modulo Codes with Summation of Weighted Transitions with Natural Number Sequence of Weights

Systematic codes with simple formation rules and low redundancy are used in the reliable digital systems design. Codes with summation with a constant number of check bits, which does not depend on data vector length, are often applied. The paper shows that error detection properties of these codes could be improved by a modification of check bits calculation rules. The authors offer the algorithm of the formation of a code with summation based on weighing the transitions between bits on neighboring positions of data vectors and calculation of the total weight of the data vector by the modulo, which is degree of two. The paper contains a detailed analysis of properties of new codes compared with known ones. The authors state the conditions of the formation of error-tolerant modulo weight-based codes with summation with the maximum number of detected errors in data vectors. Improving of error detection characteristics in new codes, compared with classic ones, is achieved by the uniform distribution of data vectors among check vectors. This, in its turn, brings some part of monotonous and asymmetric errors to the class of undetectable in addition to symmetric ones. Modulo weight-based codes detect more symmetric errors than modulo codes with summation of ones, but asymmetric errors are the dominant sort of them for almost any length of data vector. Modulo codes with summation of weighted transitions are the perspective class of systematic codes to solve the tasks of digital systems technical diagnostics.

Fast Adaptive Blind MMSE Equalizer for Multichannel FIR Systems

We propose a new blind minimum mean square error (MMSE) equalization algorithm of noisy multichannel finite impulse response (FIR) systems, that relies only on second-order statistics. The proposed algorithm offers two important advantages: a low computational complexity and a relative robustness against channel order overestimation errors. Exploiting the fact that the columns of the equalizer matrix filter belong both to the signal subspace and to the kernel of truncated data covariance matrix, the proposed algorithm achieves blindly a direct estimation of the zero-delay MMSE equalizer parameters. We develop a two-step procedure to further improve the performance gain and control the equalization delay. An efficient fast adaptive implementation of our equalizer, based on the projection approximation and the shift invariance property of temporal data covariance matrix, is proposed for reducing the computational complexity from O(n3) to O(qnd), where q is the number of emitted signals, n the data vector length, and d the dimension of the signal subspace. We then derive a statistical performance analysis to compare the equalization performance with that of the optimal MMSE equalizer. Finally, simulation results are provided to illustrate the effectiveness of the proposed blind equalization algorithm.

Blind channel identification: subspace tracking method without rank estimation

Subspace (SS) methods are an effective approach for blind channel identification. However, these methods also have two major disadvantages: 1) They require accurate channel length estimation and/or rank estimation of the correlation matrix, which is difficult with noisy channels, and 2) they require a large amount of computation for the singular value decomposition (SVD), which makes it inconvenient for adaptive implementation. Although many adaptive subspace tracking algorithms can be applied, the computational complexity is still O(m/sup 3/), where m is the data vector length. In this paper, we introduce new recursive subspace algorithms using ULV updating and successive cancellation techniques. The new algorithms do not need to estimate the rank of the correlation matrix. Furthermore, the channel length can be overestimated initially and be recovered at the end by a successive cancellation procedure, which leads to more convenient implementations. The adaptive algorithm has computations of O(m/sup 2/) in each recursion. The new methods can be applied to either the single user or the multiuser cases. Simulations demonstrate their good performance.