scholarly journals MMSE-NP-RISIC-Based Channel Equalization for MIMO-SC-FDE Troposcatter Communication Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Zedong Xie ◽  
Xihong Chen ◽  
Xiaopeng Liu ◽  
Yu Zhao
Keyword(s):  
Interference Cancellation ◽  
Communication Systems ◽  
Intersymbol Interference ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Channel Equalization ◽  
Single Input Single Output ◽  
Single Carrier ◽  
Single Output ◽  
The Impact

The impact of intersymbol interference (ISI) on single-carrier frequency-domain equalization with multiple input multiple output (MIMO-SC-FDE) troposcatter communication systems is severe. Most of the channel equalization methods fail to solve it completely. In this paper, given the disadvantages of the noise-predictive (NP) MMSE-based and the residual intersymbol interference cancellation (RISIC) equalization in the single input single output (SISO) system, we focus on the combination of both equalization schemes mentioned above. After extending both of them into MIMO system for the first time, we introduce a novel MMSE-NP-RISIC equalization method for MIMO-SC-FDE troposcatter communication systems. Analysis and simulation results validate the performance of the proposed method in time-varying frequency-selective troposcatter channel at an acceptable computational complexity cost.

scholarly journals Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements

2020 ◽  
Vol 10 (21) ◽  
pp. 7492
Author(s):  
Daniel Fernandes ◽  
Francisco Cercas ◽  
Rui Dinis ◽  
Pedro Sebastião
Keyword(s):  
Multiple Input Multiple Output ◽  
Decision Feedback ◽  
Signal Quality ◽  
Coverage Area ◽  
Propagation Models ◽  
Single Input Single Output ◽  
Single Carrier ◽  
Single Output ◽  
The Impact ◽  
Siso Systems

The demand for ubiquitous telecommunications services forces operators to have a special concern about signal quality and the coverage area they offer to their customers. This was usually checked by using suitable propagation models for Single Input Single Output (SISO) systems, which are no longer the case for new and future mobile generations, such as 5G and beyond. To guarantee good signal quality coverage, operators started to replace these models with Multiple Input Multiple Output (MIMO) ones. To achieve the best results, these models are usually calibrated with Drive Test (DT) measures; however, the DTs available for MIMO propagation models are sparse, in contrast to SISO ones. The main contribution presented in this paper is a methodology to extend the propagation models of SISO systems so they can be applied in MIMO sytems with Single-Carrier and Frequency-Domain Equalization (SC-FDE), while still using DTs acquired for SISO systems. This paper presents the impact on Bit Error Rate (BER) performance and its coverage area resulting from the application of our proposed method. We consider a MIMO SC-FDE system with an Iterative Block Decision Feedback Equalization (IB-DFE) receiver and we present the improvement expressions for the BER that we illustrate with some simulations.

scholarly journals The Impact of Oil Well Fires on the Free Space Optical Systems

2019 ◽  
Vol 29 (3) ◽  
pp. 113 ◽  
Author(s):  
Firas S. Mohammed ◽  
Farouk K. Shaker
Keyword(s):  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Rain Attenuation ◽  
Optical Systems ◽  
Oil Well ◽  
Free Space Optical ◽  
Pollution Effects ◽  
Single Input Single Output ◽  
Single Output ◽  
The Impact

Aerosol particles in oil fire plumes caused crucial air pollution. The smoke plumes from the blazes initially launched 200-400 m into the air and then continued to rise. The presence of liquid and solid aerosols may cause severe disturbance to the propagation of optical and infrared waves, thus can produce harmful effects on the wireless communication systems. In this paper, we analyze the bit error rate (BER), single to noise ratio (SNR), Q- factor and outage performance of single-input single-output (SISO) and multiple-input multiple-output (MIMO) FSO systems under attenuation of dense smoke conditions. Obtained results demonstrated that the performance of (SISO) FSO link is degraded from the Fog, Smoke and acid-rain Attenuation due to their chemical nature, their size and their concentration. As well, (MIMO) FSO link is a highly efficient way can be minimal smoke pollution effects

The impact of MIMO Communication on Non-Frequency Selective Channels Performance

2011 ◽  
pp. 100-114
Author(s):  
Andreas Ahrens ◽  
César Benavente-Peces
Keyword(s):  
Communication Systems ◽  
Mimo System ◽  
Quality Criteria ◽  
Single Input Single Output ◽  
Mimo Communication ◽  
Single Output ◽  
Transmission Modes ◽  
Input Multiple Output ◽  
Frequency Selective ◽  
Frequency Selective Channels

This chapter reviews the basic concepts of multiple-input multiple-output (MIMO) communication systems and analyses their performance within non-frequency selective channels. The MIMO system model is established and by applying the singular value decomposition (SVD) to the channel matrix, the whole MIMO system can be transformed into multiple single-input single-output (SISO) channels having unequal gains. In order to analyze the system performance, the quality criteria needed to calculate the error probability of M-ary QAM (Quadrature Amplitude Modulation) are briefly reviewed and used as reference to measure the improvements when applying different signal processing techniques. Bit and power allocation is a well-known technique that allows improvement in the bit-error rate (BER) by managing appropriately the different properties of the multiple SISO channels. It can be used to balance the BER’s in the multiple SISO channels when minimizing the overall BER. In order to compare the various results, the efficiency of fixed transmission modes is studied in this work regardless of the channel quality. It is demonstrated that only an appropriate number of MIMO layers should be activated when minimizing the overall BER under the constraints of a given fixed date rate.

scholarly journals PERFORMANCE EVALUATION OF RELAYING SCHEMES FOR WIRELESS COMMUNICATION SYSTEMS

2013 ◽  
pp. 157-161
Author(s):  
MANISHA CRASTO BRAGANC ◽  
HASANALI G. VIRANI ◽  
SHAILESH KHANOLKAR
Keyword(s):  
Communication Systems ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Multiple Input Multiple Output ◽  
Amplify And Forward ◽  
Decode And Forward ◽  
Wireless System ◽  
Single Input Single Output ◽  
Single Output ◽  
Alamouti Coding

The evaluation of MIMO (multiple-input multiple-output) Relay wireless system is carried out and compared against the performance of a SISO (single-input single-output) Relay wireless system. The encoding scheme used in MIMO is Alamouti coding and decoding is done by the Maximum Likelihood (ML) detector. A comparison is made between the SISO non-regenerative amplify-and-forward (AF) and regenerative decode-and-forward (DF) relaying schemes. The plots of bit error rate (BER) versus signal to noise ratio (SNR) are simulated by incorporating Rayleigh fading condition in the presence of additive white Gaussian noise(AWGN) using MATLAB.

scholarly journals The BER Analysis of MIMO System for M-PSK over Different Fading Channels using STBC Code Structure

2019 ◽  
Vol 8 (3) ◽  
pp. 5831-5836
Keyword(s):  
Fading Channels ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Spatial Diversity ◽  
Single Input Single Output ◽  
Single Output ◽  
Code Structure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Rician Channel

High information rates inside the restricted frequency (RF) spectrum is often fascinating that results in radios with capabilities on the far side a single-input single-output (SISO) topology. In recent days introduced wireless systems have adopted multiple-input multiple-output (MIMO) topologies that use 2 or more transmitters and 2 or more receivers to send information at the same time over same RF bandwidth. The performance of MIMO system may be improved by involving multiple antennas at transmitter and receiver therefore on offer spatial diversity. during this paper, the performance analysis of MIMO system over AWGN attenuation channel and Rician Channel with ZF receiver is bestowed. The consequences of the antenna choice can even be analyzed from the simulated results. The BER (Bit Error Rate) performance characteristics of ZeroForcing (ZF) receiver is investigated for M-PSK modulation technique over the AWGN channel and Rician Channel.

scholarly journals Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems

2020 ◽  
Author(s):  
Yu Wang ◽  
Juan Wang ◽  
Jie Yang ◽  
Wei Zhang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Decision Rules ◽  
Modulation Classification ◽  
Single Input Single Output ◽  
Automatic Modulation Classification ◽  
Single Output ◽  
Input Multiple Output

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

scholarly journals Linear detectors and precoding methods for massive MIMO

2021 ◽  
Vol 42 (2) ◽  
pp. 209
Author(s):  
Jean Marcel Faria Tonin ◽  
Taufik Abrao
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Wireless Channel ◽  
Single Input Single Output ◽  
Single Output ◽  
Minimum Mean Squared Error ◽  
Input Multiple Output

Detection in multiple-input-multiple-output (MIMO) wireless communication systems is a crucial procedure in receivers since the multiple access transmission schemes generate interference due to the simultaneous transmission along with the several antennas, unlike single-input-single-output (SISO) transmission schemes. Precoding is a technique in MIMO systems used to mitigate the effects of the channel over the received signal. Hence, it is possible to adjust continuously the transmitted information to reverse the effect of the wireless channel at the receiver side. In this work, linear sub-optimal detectors and precoders for massive MIMO (M-MIMO) systems are implemented, analyzed, and compared in terms of performance-complexity trade-off. It is also being considered numerical results in both channel scenarios: a) receiver and transmitter have perfect channel state information (CSI); b) complex channel coefficients are estimated with different levels of inaccuracy. Monte-Carlo simulations (MCS) reveal that linear zero-forcing (ZF) and minimum mean squared error (MMSE) massive MIMO detectors result in a certain robustness against multi-user interference when operating under low and medium system loading, L = K/M, thanks to the favourable propagation phenomenon arising in massive MIMO systems.

scholarly journals Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems

2020 ◽  
Author(s):  
Yu Wang ◽  
Juan Wang ◽  
Jie Yang ◽  
Wei Zhang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Decision Rules ◽  
Modulation Classification ◽  
Single Input Single Output ◽  
Automatic Modulation Classification ◽  
Single Output ◽  
Input Multiple Output

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

scholarly journals BER Analysis for Downlink MIMO-NOMA Systems over Rayleigh Fading Channels

2021 ◽  
Vol 13 (6) ◽  
pp. 93-108
Author(s):  
Vu Tran Hoang Son ◽  
Dang Le Khoa
Keyword(s):  
Fading Channels ◽  
Interference Cancellation ◽  
Rayleigh Fading ◽  
Multiple Input Multiple Output ◽  
Successive Interference Cancellation ◽  
Rayleigh Fading Channels ◽  
Total System ◽  
Single Input Single Output ◽  
Single Output ◽  
Power Domain

The Multiple-input multiple-output (MIMO) technique combined with non-orthogonal multiple access (NOMA) has been considered to enhance total system performance. This paper studies the bit error rate of two-user power-domain NOMA systems using successive interference cancellation receivers, with zeroforcing equalization over quasi-static Rayleigh fading channels. Successive interference cancellation technique at NOMA receivers has been the popular research topic due to its simple implementation, despite its vulnerability to error propagation. Closed-form expressions are derived for downlink NOMA in single-input single-output and uncorrelated quasi-static MIMO Rayleigh fading channel. Analytical results are consolidated with Monte Carlo simulation.

scholarly journals ASEP of MIMO System with MMSE-OSIC Detection over Weibull-Gamma Fading Channel Subject to AWGGN

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Keerti Tiwari ◽  
Davinder S. Saini ◽  
Sunil V. Bhooshan
Keyword(s):  
Error Rate ◽  
System Performance ◽  
Interference Cancellation ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Noise Model ◽  
Composite Fading ◽  
Special Cases ◽  
The Impact

Ordered successive interference cancellation (OSIC) is adopted with minimum mean square error (MMSE) detection to enhance the multiple-input multiple-output (MIMO) system performance. The optimum detection technique improves the error rate performance but increases system complexity. Therefore, MMSE-OSIC detection is used which reduces error rate compared to traditional MMSE with low complexity. The system performance is analyzed in composite fading environment that includes multipath and shadowing effects known as Weibull-Gamma (WG) fading. Along with the composite fading, a generalized noise that is additive white generalized Gaussian noise (AWGGN) is considered to show the impact of wireless scenario. This noise model includes various forms of noise as special cases such as impulsive, Gamma, Laplacian, Gaussian, and uniform. Consequently, generalizedQ-function is used to model noise. The average symbol error probability (ASEP) of MIMO system is computed for 16-quadrature amplitude modulation (16-QAM) using MMSE-OSIC detection in WG fading perturbed by AWGGN. Analytical expressions are given in terms of Fox-H function (FHF). These expressions demonstrate the best fit to simulation results.

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