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PAPR Reduction in MIMO-OFDM System Using Artificial Neural Network
Published in Anuj Singal, Sandeep Kumar, Sajjan Singh, Ashish Kr. Luhach, Wireless Communication with Artificial Intelligence, 2023
Khushboo Pachori, Harpreet Kaur, Sarabpreet Kaur, Manpreet Kaur
MIMO techniques can improve capacity by a factor of the minimum broadcast and receive antennas for narrow-band channels or flat-fading. It is natural to combine OFDM with spatial-temporal processing or STC (space-time coding) for wideband transmission to cope with frequency selectivity of wireless channels and gain diversity and/or capacity improvements [11–15]. As a result, MIMO-OFDM has turned into a significant better solution for future high-data-rate transmission across broadband wireless channels. MIMO-OFDM was initially suggested to employ OFDM for determining a MIMO systems with minimal ISI. From the past few decades, MIMO-OFDM is popular in various wireless communication applications viz., third generation (3G) systems, 3GPP long-term evolution (LTE), fourth-generation (4 G) systems, etc. [16,17].
Toward Green Communication in 5G
Published in Zoran S. Bojkovic, Dragorad A. Milovanovic, Tulsi Pawan Fowdur, 5G Multimedia Communication, 2020
Zoran S. Bojkovic, Dragorad A. Milovanovic, Tulsi Pawan Fowdur
Different resource allocation management systems have become a major feature of mobile multimedia communication systems [27–32], including power allocation [33,34], the bandwidth allocation [35–38], the allocation of subchannels [39] and so on, due to the demand for better EE in mobile multimedia communication systems. Multiple-input multiple-output (MIMO) systems will create separate parallel channels for the transmission of data streams, enhancing spectrum efficiency and network capacity, without raising the demand for bandwidth [40]. The multipath effect is reduced by the use of orthogonal frequency-division multiplexing (OFDM) technology, which converts frequency-selective channels into flat channels. MIMO-OFDM technologies are commonly used jointly for mobile multimedia communication systems. Nonetheless, in mobile multimedia communication systems, how to boost EE with QoS constraint is a major issue.
Coding Techniques to Improve Bit Error Rate in Orthogonal Frequency Division Multiplexing System
Published in Rajeshree Raut, Ranjit Sawant, Shriraghavan Madbushi, Cognitive Radio, 2020
Rajeshree Raut, Ranjit Sawant, Shriraghavan Madbushi
Real-time implementation of MIMO-based OFDM systems has shown that increased capacity, coverage, and reliability can be obtained practically with the use of the MIMO OFDM architecture [4]. Usually, MIMOs can be combined with any type of wireless communication standard; however, in practice, there is a significant performance enhancement of MIMO-aided OFDM over the non-MIMO OFDM technique. Capacity and coverage is improved with the help of beam forming MIMO OFDM communication systems, and it has already been tested under various extreme channel conditions. Smart antenna techniques, which use strong spatial correlation for processing the received signal by an array of antennas with beam-forming techniques, are able to provide high-directional beam-forming gain and also reduce the interference from other undesired directions under high spatial correlated MIMO channel. The three techniques, MIMO, OFDM, and beam forming, when combined, can have significant improvement in performance as compared to normal nonhybrid system.
Peak-to-Average-Power-Ratio (PAPR) Reduction Methods with Wavelet Transform in MIMO-OFDM
Published in IETE Journal of Research, 2018
In recent years, a recommended method for meeting the requirements of the wireless communication systems in frequency-selective fading channels has been the Orthogonal Frequency Division Multiplexing (OFDM) system [1]. The necessary requirements are secure communication, bandwidth efficiency, high data rate, etc. according to the location of the signals. Advantages such as high data transmission rate, bandwidth saving, and reliability are available in the OFDM systems. Moreover, the OFDM system effectively provides numerous parallel narrow band channels and is used along with the Multiple Input–Multiple Output (MIMO) systems, which in turn increases the data transmission rate, diversity gain, and system capacity [1,2]. MIMO-OFDM systems are regarded as key technology for wireless communication systems with high data rate in the current communication systems and are used in Digital Subcarrier Lines, IEEE802.11, IEEE802.16, and IEEE 802.15.3a, and satellite connections in 4G technology [3]. Despite it being a key technology, there are also problems in MIMO-OFDM systems. The Peak-to-Average-Power-Ratio (PAPR) problem is one of the most significant of these problems and there are several recommended methods to solve this [4–9]. These methods are discussed in two groups. The first group includes signal jamming methods (clipping, peak windowing, etc.). Of these methods, the clipping method is the simplest one for the PAPR reduction. However, the method jams the signal due to the out-of-band emission and interference [10].
Joint CFO and channel estimation using pilot aided interpolation for high performance MIMO-OFDM
Published in International Journal of Electronics, 2023
S. Chitra, S. Ramesh, Ramya Vijay, G. Jegan, T. Samraj Lawrence
The presence of orthogonal subcarriers and multiple antennas, the performance of MIMO-OFDM is greatly influenced by frequency synchronisation and channel estimation errors (Parna Sabeti et al., 2019). The frequency mismatch between the transmitter and receiver local oscillators, as well as the Doppler shift, are the main sources of carrier frequency offset (CFO). Intercarrier interference (ICI) arises as a result of the loss of orthogonality among the subcarriers, which degrades the performance of MIMO-OFDM (Yang, Zhang, et al., 2018). The bit error rate (BER) is the metric used to analyse the system performance degradation due to the presence of CFO (Daljeet Singh et al., 2019).
Turbo-Coded Mimo-OFDM Channel Estimation Using the Chaotic Grey Wolf Optimizer and Genetic Algorithm
Published in IETE Journal of Research, 2023
Chennapragada Padmaja, Boleti Lakshmi Malleswari
Here, the CE method overcomes the limitations of least square error(LSE) and MMSE-related CE methods using the hybrid Chaotic Grey Wolf Optimizer and the Genetic Algorithm. By this, the least square estimation-based CE and partial minimum mean square error method avoids the computational complexity. As a consequence, the channel method is exposed for the investigation and hybrid CGWO-Particle Swarm Optimization (PSO) channel model. Besides, the CE is executed in GA and CGWO initialized through LS estimation with MMSE channel modes. For the MIMO/OFDM channel model, the best channel is assessed initially by the least square and minimum mean square error individually to utilize the Chaotic Grey Wolf Optimizer, and then LS and MMSE reduce the error through the genetic algorithm for computing the best channel. Then, the MIMO/OFDM system model is used for the transmitted and received signal in transmit and receive signals from multiple amplitude signal constellations. The impulse response channel is measured below where represents the sampling interval, is the amplitude and is the delay. Hence, the received signal equation is illustrated below where specifies the discrete Fourier transform matrix, specifies the matrix with elements of on its diagonal, . After generating MIMO-OFDM, the next step is the channel estimation utilizing multiple mean square errors and the least square technique, which are explained below.