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 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 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.

An Adaptive Multiple LS Channel Estimation Method for MIMO System

2012 ◽  
Vol 239-240 ◽  
pp. 1084-1088
Author(s):  
Jin Lun Chen
Keyword(s):  
Numerical Simulation ◽  
Channel Estimation ◽  
Communication Systems ◽  
Mimo System ◽  
Estimation Method ◽  
Linear Least Squares ◽  
Mimo Communication ◽  
Channel Estimate ◽  
Wide Range ◽  
Multiple Input

Accurate channel estimation plays a key role in multiple-input and multiple-output (MIMO) communication systems. In this paper, we firstly discuss the popular linear least squares (LS) channel estimation and the multiple LS (MLS) channel estimation. Then an adaptive multiple LS (AMLS) channel estimate approach is proposed. Using numerical simulation, it is found that the proposed estimation method outperforms LS and MLS for a wide range of training SNRs.

Research on UCD Precoding for MIMO System

2012 ◽  
Vol 459 ◽  
pp. 620-623
Author(s):  
Hong He ◽  
Tao Li ◽  
Tong Yang ◽  
Lin He
Keyword(s):  
Communication Systems ◽  
Mimo System ◽  
Geometric Mean ◽  
Multiple Input Multiple Output ◽  
Symbol Error Rate ◽  
Error Correcting Codes ◽  
Decomposition Scheme ◽  
Input Multiple Output ◽  
Uniform Channel ◽  
Channel Decomposition

This article mainly describes a new technique of multiple-input multiple-output (MIMO) communication systems based on the recent communication demand. This technique, by pre-coding CSI (the channel state information) at the transmitter, is based on UCD (Uniform Channel Decomposition) algorithm for MIMO system. By Uniform Channel decomposition of channel matrix, the algorithm can decompose a MIMO downlink channel into multiple identical sub-channels. The power allocation applied to each sub channel in MIMO system is identical, and the MIMO channel’s capacity isn’t reduce when the SNR (Signal Noise Ratio) is low. The simulations show that the UCD scheme has a better performance than GMD (Geometric Mean Decomposition) scheme even without the use of error-correcting codes, and the Symbol Error Rate (SER) of UCD algorithm is lower than GMD’s at the same SNR. Consequently, MIMO system gets a better interference performance by UCD algorithm.

scholarly journals Cognitive-Femtocell Based Resource Allocation in Macrocell Network

2021 ◽  
Author(s):  
Joydev Ghosh
Keyword(s):  
Base Station ◽  
Base Stations ◽  
Network Coverage ◽  
Femtocell Networks ◽  
Single Input Single Output ◽  
Single Output ◽  
Efficiency Measures ◽  
Input Multiple Output ◽  
Single Input ◽  
Cognitive Femtocell

<div>In this paper, we articulate the network coverage issues for both Femto Users (FUs) and Macro Users (MUs) located at cell edges. The cognitive-femtocell networks functioning under the vicinity of a macrocell frontier where the parameters such as pathloss, shadowing, Rayleigh fading have considered into the system model. The users, located at network border are positioned far apart from the Macro Base Station (MBS). This can be treated as the underprivileged users. The underprivileged users are to be facilitated by the femto cell base stations to provide uninterrupted QoS. We present on the overall outage probability of Single Input single Output (SISO) users and Single Input Multiple Output (SIMO) users, respectively, by taking several circumstantial components such as such as probability density function (PDF), location gap between base stations (BSs) and users, intra-tier interference and inter-tier interference into account. Further, evaluation has been extended by considering network throughput as the efficiency measures based on the sub-carrier and the power allotment in the dual tier network.</div>

Sign in / Sign up

Export Citation Format

Share Document