Sajjad Ahmed Ghauri

A Novel Modulation Classification Approach Using Gabor Filter Network

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel.

M-PAM Signals Classification Using Modified Gabor Filter Network

A Modified Gabor Filter (MGF) network based approach is used for feature extraction and classification of -ary Pulse Amplitude Modulated (-PAM) signals by adaptively tuning the parameters of MGF network. Modulation classification of -PAM signals is done under the influence of additive white Gaussian noise (AWGN) and channel effects such as Rayleigh flat fading and Rician flat fading. The MGF network uses the network structure of two layers. First layer which is input layer constitutes the adaptive feature extraction part and second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of MGF using Recursive Least Square (RLS) algorithm. The simulation results in the form confusion matrix show that proposed modified modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR). The performance comparison with state-of-the-art existing techniques shows the significant performance improvement of proposed MGF based classifier.

Channel Estimation in Massive MIMO Systems Using Heuristic Approach

The significant forecasted increase in the number of devices and mobile data requirements has posed stringent requirements for future wireless communication networks. Massive MIMO is one of the chief candidates for future 5G wireless communication systems, but to fully reap the true benefits many research problems still need to be solved or require further analysis. Among many, the problem of estimating channel between the user terminals and each BS antenna holds a significant place. In this paper, we deal with the accurate and timely acquisition of massive Channel State Information as an optimization problem that is solved using heuristic optimization techniques i.e. Genetic Algorithm, Particle Swarm Optimization and Differential Evolution. Results have been obtained by exploiting the parallel processing property bestowed when using match filtering and beamforming for precoding and decoding respectively. Monte Carlos simulation have been presented for the purpose of performance comparison among aforementioned optimization techniques based on Mean Squared Error criterion.