scholarly journals Optimized Scheduling Technique of Null Subcarriers for Peak Power Control in 3GPP LTE Downlink

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Soobum Cho ◽  
Sang Kyu Park
Keyword(s):  
Power Efficiency ◽  
Multiple Access ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Papr Reduction ◽  
Single Input Single Output ◽  
Single Output ◽  
Multiple Input ◽  
Input Multiple Output

Orthogonal frequency division multiple access (OFDMA) is a key multiple access technique for the long term evolution (LTE) downlink. However, high peak-to-average power ratio (PAPR) can cause the degradation of power efficiency. The well-known PAPR reduction technique, dummy sequence insertion (DSI), can be a realistic solution because of its structural simplicity. However, the large usage of subcarriers for the dummy sequences may decrease the transmitted data rate in the DSI scheme. In this paper, a novel DSI scheme is applied to the LTE system. Firstly, we obtain the null subcarriers in single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, respectively; then, optimized dummy sequences are inserted into the obtained null subcarrier. Simulation results show that Walsh-Hadamard transform (WHT) sequence is the best for the dummy sequence and the ratio of 16 to 20 for the WHT and randomly generated sequences has the maximum PAPR reduction performance. The number of near optimal iteration is derived to prevent exhausted iterations. It is also shown that there is no bit error rate (BER) degradation with the proposed technique in LTE downlink system.

scholarly journals Beam division multiple access for millimeter wave massive MIMO: Hybrid zero-forcing beamforming with user selection

2022 ◽  
Vol 12 (1) ◽  
pp. 445
Author(s):  
Hong Son Vu ◽  
Kien Truong ◽  
Minh Thuy Le
Keyword(s):  
Multiple Access ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Zero Forcing ◽  
Multiuser Interference ◽  
Novel Approach ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Promising Solution

<p>Massive multiple-input multiple-output (MIMO) systems are considered a promising solution to minimize multiuser interference (MUI) based on simple precoding techniques with a massive antenna array at a base station (BS). This paper presents a novel approach of beam division multiple access (BDMA) which BS transmit signals to multiusers at the same time via different beams based on hybrid beamforming and user-beam schedule. With the selection of users whose steering vectors are orthogonal to each other, interference between users is significantly improved. While, the efficiency spectrum of proposed scheme reaches to the performance of fully digital solutions, the multiuser interference is considerably reduced.</p>

A Design Method for Stabilizing Modified Smith Predictors for Multiple-Input/Multiple-Output Time-Delay Plants

2010 ◽  
Vol 459 ◽  
pp. 221-233 ◽  
Author(s):  
Kou Yamada ◽  
Nghia Thi Mai ◽  
Yoshinori Ando ◽  
Takaaki Hagiwara ◽  
Iwanori Murakami ◽  
...  
Keyword(s):  
Time Delay ◽  
Design Method ◽  
Multiple Input Multiple Output ◽  
Smith Predictor ◽  
Single Input Single Output ◽  
Single Output ◽  
Multiple Input ◽  
Modified Smith Predictor ◽  
Input Multiple Output ◽  
Output Time

The modified Smith predictor is well known as an effective time-delay compensator for a plant with large time-delays, and several papers on the modified Smith predictor have been published. The parameterization of all stabilizing modified Smith predictors for single-input/single-output time-delay plants is obtained by Yamada et al. However, they do not examine the parameterization of all stabilizing modified Smith predictors for multiple-input/multiple-output time-delay plants. The purpose of this paper is to expand the result by Yamada et al. and to propose the parameterization of all stabilizing modified Smith predictors for multiple-input/multiple-output time-delay plants. Control characteristics of the control system using obtained parameterization of all stabilizing modified Smith predictors are also given. Finally, a numerical example is illustrated to show the effectiveness of proposed parameterization of all stabilizing modified Smith predictors.

scholarly journals A new Transmission and Reception Algorithms for Improving the Performance of SISO/MIMO- OFDM Wireless Communication System

2021 ◽  
Vol 28 (3) ◽  
pp. 146-158
Author(s):  
Maha Monther Shahab ◽  
Saad Mshhain Hardan ◽  
Asmaa Salih Hammoodi
Keyword(s):  
Wireless Communication ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Mimo Channels ◽  
Single Input Single Output ◽  
Single Output ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm ◽  
Single Input

The future wireless communication requires a reliable transmission at high data rates, so the transmission over frequency-selective fading Multiple-Input–Multiple-Output MIMO channels become interesting since the capacity of "MIMO" channels expressions enormous gains above that of their essential single-input–single-output "SISO" channels. This paper examines the performance of the Low Complexity Zero Forcing "LCZF" equalizer for both systems single-input–single-output-Orthogonal Frequency Division Multiplexing" SISO-OFDM" and spatially multiplexed-Multiple-Input–Multiple-Output "SM-MIMO-OFDM" with different "QAM" modulations. It is exploring a new algorithm to improve the performance of the "BER", spectral efficiency, and power efficiency and to reduce the complexity of the "RF" communication system under the effect of the Additive White Gaussian Noise "AWGN" and multipath fading channel. It is also improves an efficient channel by developing a Low Complexity Zero Forcing "LCZF" equalizer for both "SISO-OFDM" and "SM-MIMO-OFDM" wireless Communication systems. This is done by proposing a new algorithm at the receiver side to covert the Linear Convolution in to Cyclic Convolution by adding Zero Padding "ZP" to the channel impulse response in such a way to be the same length to the transmitted signal in the time domain which is of length N, where N is the length of "IFFT".

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 Beamforming Evaluation of 5G User Equipment through Novel Key Performance Indicators

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1319
Author(s):  
Miguel Á. García-Fernández ◽  
David A. Sánchez-Hernández
Keyword(s):  
Performance Indicators ◽  
Performance Metrics ◽  
Multiple Input Multiple Output ◽  
Key Performance Indicators ◽  
User Equipment ◽  
Future Research ◽  
Single Input Single Output ◽  
Single Output ◽  
Multiple Input ◽  
Input Multiple Output

Several standards exist for testing the radiated “over the air” (OTA) performance of 5G User Equipment (UE) devices. All these standards are limited to a Single-Input Single-Output (SISO) evaluation of the 5G device. With 5G networks and devices already deployed and in operation, it becomes necessary to develop realistic Multiple-Input Multiple-Output (MIMO) and beamforming performance metrics for 5G UEs. Since the most important feature of 5G is beamforming, this paper reviews the main challenges to realistically evaluate 5G device performance and proposes several novel key performance indicators (KPIs). The results obtained by some measurements show that it is possible to derive figures of merit that address the complexity of beamforming and MIMO-based 5G performance of devices in a much better way compared with the existing SISO KPIs, and that academia should provide advanced future research on these more realistic KPIs for the industry to face the challenges ahead with a better analysis of the problem in hand.

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