This paper addresses the performance of MIMO-OFDM communication system in environments where the interfering noise exhibits non-Gaussian behavior due to impulsive phenomena. It presents the design and simulation of an iterative technique that aims to minimize the effect More
This paper addresses the performance of MIMO-OFDM communication system in environments where the interfering noise exhibits non-Gaussian behavior due to impulsive phenomena. It presents the design and simulation of an iterative technique that aims to minimize the effect of impulsive noise on the performance of the MIMO-OFDM communication system under Additive White Gaussian Noise (AWGN) channel. This is a new method to recover the signals corrupted by impulsive noise in MIMO-OFDM systems over In-home Power Line Channel. The location and amplitude Impulsive noise at the receiver using an adaptive threshold to be determined. Reduced Impulsive noise effects using the mask based on the soft decision method. By iteration, the original signal estimation can be used to improve the impulsive noise estimation. This continuous loop impulsive noise detection and mitigation a better estimate of the original signal is obtained. The Bit Error Rate (BER) performance of the MIMO-OFDM system in an impulsive noise environment was evaluated. The results show the superiority and robustness of the proposed method.
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Automatic modulation classification (AMC) in detected signals is an intermediate step between signal detection and demodulation, and is also an essential task for an intelligent receiver in various civil and military applications. In this paper, we propose a semi-superv More
Automatic modulation classification (AMC) in detected signals is an intermediate step between signal detection and demodulation, and is also an essential task for an intelligent receiver in various civil and military applications. In this paper, we propose a semi-supervised Large margin AMC and evaluate it on tracking the received signal to noise ratio (SNR) changes to classify all forms of signals in a cognitive radio environment. To achieve this objective, two structures for self-training of large margin classifiers were developed in additive white Gaussian noise (AWGN) channels with priori unknown SNR. A suitable combination of the higher order statistics and instantaneous characteristics of digital modulation are selected as effective features. Simulation results show that adding unlabeled input samples to the training set, improve the tracking capacity of the presented system to robust against environmental SNR changes.
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One of the main reasons for switching to the next generation of communication systems is the demand of increasing capacity and network connections. This goal can be achieved using massive multiple input - multiple output (massive-MIMO) systems in combination with Non-or More
One of the main reasons for switching to the next generation of communication systems is the demand of increasing capacity and network connections. This goal can be achieved using massive multiple input - multiple output (massive-MIMO) systems in combination with Non-orthogonal multiple access (NOMA) technique. NOMA technology uses the successive interference cancellation (SIC) receiver to detect user’s signals which imposes an additional complexity on the system. In this paper, we proposed two methods to reduce the system complexity. The proposed method despite imperfect channel state information (CSI) in the receiver, there is not significantly reduction in the system performance. Since the computation of matrices inverse has a high computational complexity, we used the Neumann series approximation method and the Gauss-Seidel decomposition method to compute matrices inverse in the SIC receiver. Simulation results are provided at the end of the paper in terms of bit error rate (BER) at the receiver which show, these methods have lower computational complexity in comparison with the traditional methods while they cause a slight performance reduction in the SIC receiver. Also, we examined the increasing and decreasing value of imperfect channel state information in the system performance which shows the increasing value of imperfect channel state information, cause a slight performance reduction in SIC receiver.
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