FebICA: Feedback Independent Component Analysis for Complex Domain Source Separation of Communication Signals

Blind single-channel source separation is a long-standing machine learning and signal processing problem. Traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints. Generative adversarial network (GAN) are free from statistical constraints and samples, but the role of adversarial training in the task of BSS has not been fully demonstrated. Therefore, a new separation network model that enables to learn the known separated signal distribution from the stepwise fine estimation of the unknown mixture distribution was presented in this paper, and a self-attention mechanism was introduced to solve the problem of the blurring details of the generated image by the generator which preserves image details in the process of image separation. Compared with the existing single-channel blind source separation algorithm based on generative adversarial network-neural egg separation (NES), the detailed information of this new separation algorithm is more prominent, and the source signal in the mixed image has been separated more effectively, and has better separation performance than the classic blind source separation algorithms.

Electroencephalogram (EEG) extraction has widely used Stone's Blind Source Separation (Stone's BSS) algorithm. However, Stone's BSS algorithm is sensitive to the initial half-life (hlong, hshort) and weight vector W parameters, which affect the convergence of the algorithm. This paper proposes a hybridization of Stone's BSS with Particle Swarm Optimization (PSO) to boost the separation process. An improved Stone's BSS (ISBSS) method is employed to reject eye blinking from the electroencephalogram (EEG) mixture. The electroencephalogram (EEG) mixed-signal is first centralized and whitened; then, it is incorporated into the particle swarm optimization (PSO) iterative algorithm to process the initial (hlong, hshort) and generate the weight vector W parameters randomly. Finally, the generalized eigenvalue decomposition (GEVD) method is applied to extract EEG singles to obtain a clean EEG signal. A clinical EEG database is used to test the improved and other algorithms. The GEVD method estimates the measurement performance of the proposed algorithm using a carrier-to-interference ratio and integral square error and compares the proposed algorithm with the conventional Stone's BSS, fast independent component analysis (FastICA), evolutionary fast independent component analysis (EFICA), and joint approximate diagonalization of eigen matrices (JADE) algorithms to check its effectiveness. The results show that the suggested hybrid method has a better performance and decreasing elapsed time than conventional Stone's BSS and other algorithms.

During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments.

In this paper, we investigate link adaption in massive Multiple-Input and Multiple-Output(MIMO) system, where the base station is equipped with a large number of antennas. Specifically for the linear minimum mean square error (MMSE) receiver at the base station, the achievable ergodic rate is used to calculate channel quality indicator (CQI) for each user equipment (UE). We propose a rate calculation method based on the free deterministic equivalent, which relies on the statistical channel state information (CSI). The proposed method has much lower computational complexity than the conventional link adaption method such as exponential effective signal to interference plus noise ratio (SINR) mapping (EESM). Simulation results show that the the spectral efficiency obtained by the proposed method can approach almost the same level as EESM, while much lower computational complexity is needed.

Upgrading Sparse NMF algorithm for blind source separation through Adaptive Parameterized Hybrid Kernel based approach

This study considers the joint transmission precoding design for downlink multicell multiuser multiple‐input single‐output systems where imperfect channel variances are available at the base stations. The authors aim to tackle the robust signal‐to‐interference‐plus‐noise ratio (SINR) balancing problem to maximise the minimum worst‐case user rate. To solve the non‐convex problem, a duality relationship between the downlink max–min worst‐case SINR optimisation problem and the virtual uplink min–max worst‐case SINR optimisation problem is first revealed. Based on this, a new algorithm is developed to solve the virtual problem by using jointly the sub‐gradient method and the geometric programming methods, whose achieved solution is finally converted to the downlink. Their analysis shows that the proposed algorithm is guaranteed to converge and has lower computational complexity than conventional approaches. Moreover, computer simulations validate the effectiveness of the proposed method and show that the proposed algorithm has fast convergence and achieves a performance close to that of the brute search method.

A simplified group interference cancelling (IC) approach is investigated for asynchronous direct-sequence code-division multiple access on flat fading channels. The technique employs grouping by estimated signal-to-noise-plus-interference ratio (SNIR), and interference cancellation is performed blockwise, for a subset of the total number of users. We consider long random spreading codes, and include the effects of imperfect amplitude, carrier phase, and delay estimation. Performance of the technique shows SNIR gains of several dB, and concomitant improvements in error probability, with lower computational complexity than that of parallel or serial interference cancelling techniques. We also show that our SNIR expressions are applicable to both the AWGN and flat fading channels, and for moderate near–far conditions. In addition, we determine optimal group sizes for our technique, where optimality is in terms of average error probability over all users. Copyright © 2006 John Wiley & Sons, Ltd.

Physical layer security, aiming at preventing the confidential information from eavesdroppers, has drawn significant attentions in recent years. In this paper, we address the problem of securing downlink single-user and multiuser transmissions in code division multiple-access systems. Particularly, we propose a novel binary signature design method to minimize the reception of the confidential messages at the eavesdropper. The popular signal-to-interference-plus-noise ratio (SINR) metric is adopted as a pragmatic security performance measurement. Specifically, for securing the single-user transmission, we aim to design a binary signature to minimize the eavesdropper's SINR while maintain the legitimate user's SINR at a desired level. However, the optimal exhaustive searching method has complexity growing exponentially with the signature length. Thus, we propose two suboptimal low-complexity semi-definite relaxation (SDR)-based and Rayleigh-quotient-based binary signature design algorithms, which have near optimal performance with much lower computational complexity. Furthermore, we extend the findings in the single-user case to multiuser scenario. Particularly, we propose to design a set of quasi-orthogonal signatures based on eavesdropping channel and select the conditionally optimal binary signature for each user iteratively until convergence. Simulation studies confirm our analytical performance predictions and demonstrate the benefits of the proposed secure signature design algorithms.

The Signal‐to‐Interference‐plus‐Noise Ratio (SINR) is an important metric of wireless communication link quality. SINR estimates have several important applications. These include optimizing the transmit power level for a target quality of service, assisting with handoff decisions and dynamically adapting the data rate for wireless Internet applications. Accurate SINR estimation provides for both a more efficient system and a higher user‐perceived quality of service. In this paper, we develop new SINR estimators and compare their mean squared error (MSE) performance. We show that our new estimators dominate estimators that have previously appeared in the literature with respect to MSE. The sequence of transmitted bits in wireless communication systems consists of both pilot bits (which are known both to the transmitter and receiver) and user bits (which are known only by the transmitter). The SINR estimators we consider alternatively depend exclusively on pilot bits, exclusively on user bits, or simultaneously use both pilot and user bits. In addition, we consider estimators that utilize smoothing and feedback mechanisms. Smoothed estimators are motivated by the fact that the interference component of the SINR changes relatively slowly with time, typically with the addition or departure of a user to the system. Feedback estimators are motivated by the fact that receivers typically decode bits correctly with a very high probability, and therefore user bits can be thought of as quasipilot bits. For each estimator discussed, we derive an exact or approximate formula for its MSE. Satterthwaite approximations, noncentral F distributions (singly and doubly) and distribution theory of quadratic forms are the key statistical tools used in developing the MSE formulas. In the case of approximate MSE formulas, we validate their accuracy using simulation techniques. The approximate MSE formulas, of interest in their own right for comparing the quality of the estimators, are also used for optimally combining estimators. In particular, we derive optimal weights for linearly combining an estimator based on pilot bits with an estimator based on user bits. The optimal weights depend on the MSE of the two estimators being combined, and thus the accurate approximate MSE formulas can conveniently be used. The optimal weights also depend on the unknown SINR, and therefore need to be estimated in order to construct a useable combined estimator. The impact on the MSE of the combined estimator due to estimating the weights is examined. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004

Comparison of downlink transmission strategies for distributed antenna systems with multi-antenna arrays

In wireless communication systems with multiple users, the near-far effect and the cochannel interferences decrease the spectral efficiency and system performance. An efficient known method to combat these destructive effects is to properly adjust the transmit powers. A practical and efficient way for power control (PC) in cellular communication systems is the autonomous distributed PC (DPC). In this method, the transmit power of each link is independently modified based on the received signal-to-interference-plus-noise ratio (SINR) reported from the other side of that link. It is known that in a perfect scenario where the reported SINRs are exact, this algorithm converges to the optimum transmit powers. However, in the case of erroneous SINR reports, the transmit powers deviate from their optimum values. This paper addresses the destructive effect of inaccurate SINR reports on the performance of a DPC algorithm. Then, independent variable step-sizes that update the transmit powers are proposed, and convergence of the proposed DPC algorithm to the optimum solution of the original problem is analytically shown. Additionally, a protocol to adaptively tune the step-sizes in our algorithm, which improves the performance of PC in the presence of inaccurate SINR reports, is proposed. Computer simulations are used to study the performance of the proposed method in terms of both the speed of converges and the steady-state accuracy of link qualities.

This paper discusses the separation of signals in multiple-tag radio frequency identification (RFID) systems. First, a model for the RFID system in both single and multiple tag environments is presented. Then, an analytical constant modulus algorithm (ACMA) for the blind source separation problem is reviewed. An alternative approach to the traditional ACMA using joint diagonalization is considered. Finally, both the ACMAs are applied to the multiple-tag RFID environment and performance of the system is studied. Simulations are carried out for 4-QAM and 16-QAM modulations at tag and analyses of the simulation results reveal that the ACMA with joint diagonalization takes lesser CPU time to execute compared to the traditional ACMA, but with higher average modulus error (AME). The variation of system performance with the number of measurements, SNR (signal to noise ratio), number of tags and number of antennas at the reader is also studied. Based on these, some design guidelines are presented. Interestingly both the ACMA algorithms work for the 16-QAM case and yield trends similar to those of 8-PSK and 4-QAM.

Selective per antenna rate control (S-PARC) technique has been decided to be deployed in time division-synchronous code division multiple access (TD-SCDMA) high speed packet access plus (HSPA+) system. In this paper, we propose an enhanced data stream selection algorithm for the proposal of S-PARC to improve Node B average throughput and initial block error rate (BLER) by pre-processing signal to interference plus noise ratio (SINR). The new algorithm considers the impact of channel fluctuation, Node B average initial BLER and continue scheduling of the same UE. It is shown that the proposed scheme can achieve remarkable improvement of system performance, especially when UEis under high speed mode, and it also has advantages in the following ways: little modification for current specification, low computational complexity and no additional controlling signals.

In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax).

An angle error extraction algorithm based on JADE for three-channel radar seeker system with the existence of deception jamming