Presence Of non-Gaussian Interference Research Articles

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs).

Multi-antenna intelligent spectrum sensing in the presence of non-Gaussian interference

The secondary user (SU) only detects one feature of the primary user (PU) signal in conventional spectrum sensing. However, single feature detection does not fully explore the PU information. Meanwhile, non-Gaussian interference will also affect the spectrum sensing performance. In this paper, we propose a novel spectrum sensing method using multimodal fusion and convolutional neural networks (CNN). First, the received signal at multiple antennas is preprocessed, where the generalized covariance matrix and the generalized Wigner-Ville distribution are used to characterize two different modes of the received signals as the input of CNN. Then, a CNN model using multimodal fusion is constructed. In this model, the attention mechanism is first used to extract features of different modalities, and the multimodal fusion technique fuses the extracted features to obtain the feature vector. Finally, the detection statistics and detection thresholds for spectrum sensing are constructed and compared using the output feature vectors of CNN. Simulation results show that the performance of multi-mode fusion spectrum sensing is better than the single-mode counterpart. This method can effectively realize spectrum sensing with both Gaussian noise and non-Gaussian interference, and it has a high detection probability at low SNR.

Studying the properties of a robust algorithm for identifying linear objects, which minimizes a combined functional

This paper addresses the task of identifying the parameters of a linear object in the presence of non-Gaussian interference. The identification algorithm is a gradient procedure for minimizing the combined functional. The combined functional, in turn, consists of the fourth-degree functional and a modular functional, whose weights are set using a mixing parameter. Such a combination of functionals makes it possible to obtain estimates that demonstrate robust properties. We have determined the conditions for the convergence of the applied procedure in the mean and root-mean-square measurements in the presence of non-Gaussian interference. In addition, expressions have been obtained to determine the optimal values of the algorithm's parameters, which ensure its maximum convergence rate. Based on the estimates obtained, the asymptomatic and non-asymptotic values of errors in estimating the parameters and identification errors. Because the resulting expressions contain a series of unknown parameters (the values of signal and interference variances), their practical application requires that the estimates of these parameters should be used. We have investigated the issue of stability of the steady identification process and determined the conditions for this stability. It has been shown that determining these conditions necessitates solving the third-degree equations, whose coefficients depend on the specificity of the problem to be solved. The resulting ratios are rather cumbersome but their simplification allows for a qualitative analysis of stability issues. It should be noted that all the estimates reported in this work depend on the choice of a mixing parameter, the task of determining which remains to be explored. The estimates obtained in this paper allow the researcher to pre-evaluate the capabilities of the identification algorithm and the effectiveness of its use in solving practical problems.

Робастна ідентифікація об'єктів на основі мінімізації комбінованого функціоналу

Розглянуто задачу ідентифікації параметрів лінійного об'єкта за наявністю негаусівських завад на основі мінімізації комбінованого функціоналу, який поєднує властивості МНК та МНМ та забезпечує робастність одержуваних оцінок. Досліджено статистичні властивості градієнтного алгоритму ідентифікації, в результаті чого визначено умови його збіжності в середньому і середньоквадратичному. Для дослідження властивостей в алгоритму використано функції Ляпунова. Отримано аналітичні оцінки неасимпотичних та асимптотичних значень помилки оцінювання параметрів і точності ідентифікації. Показано, що ці значення помилки оцінювання і точності ідентифікації залежать від вибору параметра змішування. Отримані оцінки є досить загальними і залежать від статистичних характеристик корисних сигналів і завад. Тому для їх практичного застосування слід скористатися оцінками цих параметрів. Результати експериментального дослідження властивостей алгоритму при ідентифікації об’єкту, вихід якого вимірюється з незалежним шумом з розподілом Релея, некорельованим з сигналами, свідчать про ефективність підходу, що розвивається. Отримані в роботі оцінки дозволяють досліднику при вирішенні практичних завдань попередньо оцінити можливості досліджуваного алгоритму і ефективність його застосування.

Robust object identification in the presence of non-Gaussian interference

The problem of identifying the parameters of a linear object in the presence of non-Gaussian interference is considered based on minimizing a combined functional that combines the properties of OLS and IIS. The conditions for the convergence of the gradient identification algorithm in mean and mean square are determined. Analytical estimates are obtained for non-asymptotic and asymptotic values of the parameter estimation error and the identification accuracy. It is shown that these values of the estimation error and identification accuracy depend on the choice of the mixing parameter.

Nonparametric trellis equalization in the presence of non-gaussian interference

We consider the problem of trellis equalization of the intersymbol interference channel in the presence of thermal noise and cochannel interference (CCI). Conventional maximum-likelihood sequence estimation (MLSE) and maximum a posteriori probability (MAP) trellis equalizers treat the sum of noise and interference as additive white Gaussian noise, while CCI is generally a colored non-Gaussian process. We propose a novel nonparametric approach based on the estimation of the probability density function of the noise-plus-interference. Given the availability of a limited volume of data, the density is estimated by kernel-smoothing techniques. The use of a whitening filter in the presence of temporally colored disturbance is also addressed. Simulation results are provided for the global system for mobile communications (GSM), showing a significant performance improvement with respect to the equalizer based on the Gaussian assumption. Major advantages of the proposed strategy are its intrinsic robustness and general applicability to those cases where accurate modeling of the interference is difficult or a model is not available.