Modified constant modulus algorithm based on bat algorithm

In this work, modified constant modulus algorithm based on bat algorithm is proposed for wireless sensor communications systems. The bat algorithm is a swarm intelligence optimization algorithm, which mainly used to solve optimization problems. The proposed algorithm focused on modified constant modulus algorithm, which is also applicable to the constant modulus algorithm. The error function of blind equalization algorithm is used as the evaluation function of the bat algorithm, and then the initial value of the weight vector is calculated adaptively by the bat algorithm. Theoretical analysis is provided to illustrate that the proposed algorithm has a faster convergence speed than the original one and is suitable for almost all blind channel equalization algorithms. The simulation results support the newly proposed improved algorithm. The proposed algorithm could be applied to some more complex wireless channel environments to improve the reception performance of sensor communication systems.

MIMO OFDM Blind Channel Equalization using Multilayer Neural Network in Impulsive Noise Environment

Multiple Input Multiple Output (MIMO) system has several input and output antennas for executing the data transmission. Channel Estimation (CE) is required in MIMO, to achieve the effective signal transmission over the various amount of antennas. By using CE over the MIMO, the noiseless data transmission is performed. Hence in this paper, a Multi-layer Neural Network (MNN) is used for identifying the CE and this system is named as Multi-layer Neural Network-MIMO-Digital Filter (MNN-MIMO-CE) is proposed for blind channel equalization. The MNN-MIMO-CE has Feed forward Artificial Neural Network (FANN) with back propagation in Levenberg-Marquardt (LM) algorithm and it has two processes MNN training and MNN testing. LM algorithm is used to train the MNN. These processes are used to provide the CE for different combination of antennas. The performance of the MNN-MIMO-CE method is evaluated in comparison with the existing method [25] through simulations using BER as the performance measure.

Moving Target Indication for Dual-Channel Circular SAR/GMTI Systems

In a dual-channel circular synthetic aperture radar (CSAR) and ground moving target indication (GMTI) system, the antenna baseline is not parallel with the flight path due to a yaw angle. The angle causes a varying group-phase shift between the dual-channel signals and therefore degrades the correlation between the image pair. Therefore, the group-phase shift needs to be removed before channel equalization. To resolve the problem, the interferometric phase term was deduced and analyzed based on the geometry of a dual-channel CSAR system. Then, the varying phase term with respect to the Doppler frequency and the varying group-phase shift over the range were compensated for in the channel registration. Furthermore, blind channel equalization, including two-dimensional calibration and amplitude equalization, was applied to eliminate the amplitude and residual phase differences between the channels. Finally, the amplitude image obtained using a displaced phase center antenna (DPCA) was multiplied by the phase image obtained with along-track interferometry (ATI) to detect moving targets. The experimental results verified the effectiveness of the method for both uniform and non-uniform clutter suppression.

Blind Channel Equalization of Star QAM using Dual Dispersion MCMA Algorithm

An algorithm for blind channel equalization is presented for 16 and 32 Star QAM, namely, Dual Dispersion MCMA algorithm. The algorithm taking the concept from MCMA, uses the Dual Dispersion minimization approach for blind channel equalization. As Star QAM constellation contains two rings, so instead of one, dual dispersion minimization approach is used for its both rings. With modification in MCMA cost function, the new algorithm results improved performance in convergence rate of Residual ISI and MSE against MCMA algorithm. By incorporating decision directed approach, the performance increases drastically. Simulation results show effectiveness of proposed algorithm in removing the ISI and correcting the errors in symbols of received signal.

Passive Vector Sensing for Non-Cooperative Emitter Localization in Indoor Environments

Indoor emitter localization is a topic of continued interest for improving wireless security as wireless technologies continue to become more advanced. Conventional methods have focused on the localization of devices relative to multi-sensor systems owing to ease of implementation with pre-existing infrastructures. This work, however, focuses on enhancing wireless security via non-cooperative emitter localization in scenarios where only a single receiver can be employed. A vector sensor is simulated and experimentally developed that extracts three-dimensional signal characteristics for room-based emitter localization and is compared to conventional methodologies such as Received Signal Strength (RSS), Time of Arrival (ToA), and Direction of Arrival (DoA). The proposed method generates time-frequency fingerprints and extracts features through dimensionality reduction. A second stage extracts spatial parameters consisting of Channel State Information (CSI) and DoAs that are analyzed using a Gaussian Mixture Model (GMM) to segregate fine-grained regions of interest within each room where the non-cooperative emitter resides. Blind channel equalization cascaded with a least squares channel estimate is used for acquiring the CSI, whereas the DoAs are obtained by unique trigonometric properties of the vector sensing antenna. The results demonstrate that a vector sensor can improve non-cooperative emitter localization and enhance wireless security in indoor environments.

Effects of blind channel equalization using the regressive accelerator algorithm version ɣ

We present a blind channel equalization scheme, applied to ɣ version regressive acceleration algorithm, which uses self-taught equalization techniques to study the characteristics of both, the second and the higher order moments for the transmitted signal, used to calculate the signal of error and thus, to make an optimal estimation of the transmitted symbols. This way, simulations of the obtained results are done in comparison with the algorithms based on the stochastic gradient and with the Bussgang algorithms. The results of that simulations show how, using the regressive acceleration algorithm version ɣ, a better detection of transmitted bits and higher convergence speeds are obtained, with a minimum mean square error.