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In this paper, we explore the physical propagation environment of radio waves by describing it in terms of distant scattering clusters. Each cluster consists of numerous scattering objects that may exhibit certain statistical properties. By utilizing geometry-based methods, we can study the channel second-order statistics (CSOS), where each distant scattering cluster corresponds to a CSOS, contributes a portion to the Doppler spectrum, and is associated with a state-space multiple-input and multiple-output (MIMO) radio channel model. Consequently, the physical propagation environment of radio waves can be modeled by summing multiple state-space MIMO radio channel models. This approach offers three key advantages: simplicity, the ability to construct the entire Doppler power spectrum from multiple uncorrelated distant scattering clusters, and the capability to obtain the channels contributed by these clusters by summing the individual channels. This methodology enables the reconstruction of the radio wave propagation environment in a simulated manner and is crucial for developing massive MIMO channel models. [ABSTRACT FROM AUTHOR]

Copyright of Informacije MIDEM: Journal of Microelectronics, Electronic Components & Materials is the property of MIDEM Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For underwater blue–green light multiple-input multiple-output orthogonal frequency division multiplexing modulation system (MIMO-OFDM), the underwater MIMO channel gain matrix corresponding to the change of receiver spacing was analyzed and derived, and a proportional power allocation scheme combined with singular value decomposition (SVD) precoding was proposed, in order to eliminate inter-channel interferences of the MIMO system. The simulation results show that when the transmitter spacing is fixed, and the receivers adopt the same spacing as the transmitters, which is difficult to achieve in the complex underwater environment, the system bit error rate (BER) performance under the proposed proportional power allocation scheme is the same as that of the average power allocation. When the receiver spacing increases, the system BER under the proposed scheme can ensure better overall performance compared with that of the traditional average allocation scheme. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 × 10 5 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods. [ABSTRACT FROM AUTHOR]

The paper proposes a novel iterative channel estimation technique for multipath fading channels in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing(MIMO-OFDM)systems with Gaussian noise and impulse noise. MIMO-OFDM systems can be beneficial if the estimated channel quality is assured at the receiver. The paper also proposes an auxiliary pilot for channel re-estimation based on some reliably retrieved data symbols. In subsequent iterations, adding new auxiliary pilots improves channel estimation accuracy. The correlation between the data channel coefficients and the original pilots is used to select reliable data. Since iterative channel estimation is time-consuming, using a channel estimator with a low processing time and computational complexity is imperative. An easy-to-implement baseline least squares(LS)method is used in the proposed estimator. According to the simulation results, the proposed iterative channel estimation technique improves MIMO-OFDM performance by increasing the number of pilots in subsequent iterations compared to the conventional channel estimation techniques.

Although the underwater optical communication system (UWOC) plays an important role in many marine applications, the acquisition, pointing and tracking (APT) problems are still great challenges in a long or turbid water channel. The Orthogonal Frequency Division Multiplexing (OFDM) and the space-time block coding (STBC) technologies have been introduced in UWOC systems to improve spectral efficiency and data transmission reliability. On these foundations, a multi-input multi-output (MIMO) mode is proposed and verified in this article to reduce the alignment requirement. In experiments, two sets of 450 nm blue laser diodes (LDs) and PIN photodiodes are selected as light sources and receivers, and then parallel light beams are generated and modulated using an Alamouti coding for its anti-interference ability. The receiving plane is rotated to simulate the various alignment situations, and the system performance of MIMO-OFDM and single-input single-output OFDM (SISO-OFDM) are compared in tap water and turbid water. The results show that the spatial diversity in MIMO can extend the communication range and its single-sided maximum detection angle range is 92% and 112% higher than the SISO system, respectively.

Service providers in 4G wireless communication networks are constantly challenged to accommodate more customers within a limited allotted capacity. OFDM (Orthogonal Frequency Division Multiplexing) is used to boost the data rate of a wireless medium while reducing interference. It is important to analyse the performance of the FFT- and DWT-OFDM by using different methods and algorithms. In this paper FFT and DWT is employed with different algorithms such as Cooley-Tukey, Prime factor, Haar and Daubechies. These algorithms have been modeled and analysed for the designed software model. A Comparative analysis has been carried out by considering SNR (Signal-to-Noise Ratio) and BER (Bit Error Rate) values for QAM modulation. MATLAB simulation showed that DWT-based OFDM system performed much better than the FFT-based OFDM systems on AWGN channel. 5G technology involves communication engineering and embedded system engineering to enhance the required data rate for different applications. The proposed system implementation with the 5G MIMO-OFDM on Zynq UltraScale provides a data rate within a range of 10 Gbps to 50 Gbps for the different order of the combinations of FFT/IFFT or DWT/IDWT and QAM. The different orders of the combinations were achieved through programmability and reconfigurability. The performance of the DWT-OFDM is more complex and computationally intensive than that of FFT-OFDM in hardware. In future wok the proposed system could be improved so as to become a multi-user MIMO(MU-MIMO).

In this paper, we explore the physical propagation environment of radio waves by describing it in terms of distant scattering clusters. Each cluster consists of numerous scattering objects that may exhibit certain statistical properties. By utilizing geometry-based methods, we can study the channel second-order statistics (CSOS), where each distant scattering cluster corresponds to a CSOS, contributes a portion to the Doppler spectrum, and is associated with a state-space multiple-input and multiple-output (MIMO) radio channel model. Consequently, the physical propagation environment of radio waves can be modeled by summing multiple state-space MIMO radio channel models. This approach offers three key advantages: simplicity, the ability to construct the entire Doppler power spectrum from multiple uncorrelated distant scattering clusters, and the capability to obtain the channels contributed by these clusters by summing the individual channels. This methodology enables the reconstruction of the radio wave propagation environment in a simulated manner and is crucial for developing massive MIMO channel models.

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on. [ABSTRACT FROM AUTHOR]

In this paper, the integration of the radio over fiber technology into a passive optical network (RoF-PON) is proposed to simultaneously distribute wired and two wireless standards each one transports 2 × 2 multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) signals. This simultaneous transmission is operated based on a single centralized optical wavelength and enabled by a polarization division multiplexing technique. The proposed RoF-PON system offers high spectral efficiency and is designed for unlicensed industrial, scientific, and medical bands at 2.4 and 5.8 GHz. The system performance is assessed by measuring the bit error rates (BERs) according to different access distances up to 30 km. After determining the optimum extinction ratio (ER) to modulate the baseband wired signal that allows jointly optical modulation of wireless signals using the same optical wavelength; all the 2 × 2 quadrature amplitude modulation (QAM) MIMO-OFDM wireless signals with a wired signal are successfully delivered without using forward error correction limit. Additionally, the system reliability has been confirmed by adopting other modulations schemes such as 8 and 16 phase-shift keying (PSK) that maintained good constellation diagrams performances. Whereas, the wired signal is analyzed according to its BER and quality Q-factor including high-quality eye diagram patterns. Also, the results revealed that the MIMO-OFDM signals mapped by 16 QAM scheme are given a better performance than PSK schemes when they are accompanied with high data rate baseband wired signal via a PON distance of 30 km. [ABSTRACT FROM AUTHOR]

Nowadays, there are massive demands on the broadband multimedia services. Data security in today's world is an essential issue in different aspects of life such as marketing services, defense, financial, and health care. Modulation schemes, channel coding, diversity techniques, etc., are types of physical layer security methods, which are used to ensure the data from unauthorized access. In addition, various cryptographic techniques are usually used to secure data transmission between sender and the intended receiver. The combination of cryptographic techniques and physical layer security schemes makes it better for transferring multimedia data through unsecure or potentially risky communication (wired and wireless) path. Many chaotic-based encryption algorithms have been suggested to ensure confidentiality and security for multimedia data over an insecure wireless channel. Cryptography techniques do not involve any system complexity but add a little computation complexity. Use of advanced processors mitigates the computational burden. In this paper, we have discussed efficient transmission of an encrypted color image through a multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO–OFDM) system over a Rayleigh fading with an additive white Gaussian noise channel. Four schemes of chaotic-based encryption are employed. Both 2 × 2 MIMO - OFDM and 4 × 4 MIMO - OFDM wireless communication systems are presented. Peak signal-to-noise ratio metric is used to evaluate the quality of the received image at different signal-to-noise ratios. Other metrics such as number of changing pixel rate, unified averaged changed intensity, correlation coefficient, entropy, and histogram are evaluated. [ABSTRACT FROM AUTHOR]

In this paper, low complexity rate and power optimization schemes operating in the spatial and frequency domains are proposed in a cognitive radio (CR) setting involving multi-user multiple-input-multiple-output-orthogonal frequency division multiplexing (MU-MIMO-OFDM). Under the assumption of a perfect secondary channel state information (CSI) at the receiver, the presented architectures encompass two main stages. In the first one, spatial power waterfilling-like method is performed per each MIMO subchannel pertaining to each subcarrier of each secondary user (SU). The resulting allocated power per each eigen-channel is considered as the power budget in the second stage. In this latter, stochastic algorithm-based approach wherein the transmit parameters per each subcarrier of each SU are adapted such that to maximize the achievable sum-rate capacity of the SUs. Three different schemes are introduced in this work. First, the derivation of the continuous rate MU-MIMO-OFDM-CR version, referred to as C-MU-MIMO-OFDM-CR is presented. Obviously, this proposition is theoretical and is taken as a benchmark for the two remaining counterparts. The second proposition we called discrete-rate MU-MIMO-OFDM-CR, and briefly designated as D-MU-MIMO-OFDM-CR which is to round the provided allocated rate. Finally, the third modified solution, denoted as P-D-MU-MIMO-OFDM-CR proceeds in a similar way as the D-MU-MIMO-OFDM-CR alternative, but superimposes the non/over-used amount of power to the power budget in next iteration. The simulation results show that, compared to the discrete rate D-MU-MIMO-OFDM-CR solution, the P-D-MU-MIMOOFDM-CR approach exhibits an approximate power gain of 1 dB when the SNR level is low, and of 5 dB at high SNR range. [ABSTRACT FROM AUTHOR]

Service providers in 4G wireless communication networks are constantly challenged to accommodate more customers within a limited allotted capacity. OFDM (Orthogonal Frequency Division Multiplexing) is used to boost the data rate of a wireless medium while reducing interference. It is important to analyse the performance of the FFT- and DWT-OFDM by using different methods and algorithms. In this paper FFT and DWT is employed with different algorithms such as Cooley-Tukey, Prime factor, Haar and Daubechies. These algorithms have been modeled and analysed for the designed software model. A Comparative analysis has been carried out by considering SNR (Signal-to-Noise Ratio) and BER (Bit Error Rate) values for QAM modulation. MATLAB simulation showed that DWT-based OFDM system performed much better than the FFT-based OFDM systems on AWGN channel. 5G technology involves communication engineering and embedded system engineering to enhance the required data rate for different applications. The proposed system implementation with the 5G MIMO-OFDM on Zynq UltraScale provides a data rate within a range of 10 Gbps to 50 Gbps for the different order of the combinations of FFT/IFFT or DWT/IDWT and QAM. The different orders of the combinations were achieved through programmability and reconfigurability. The performance of the DWT-OFDM is more complex and computationally intensive than that of FFT-OFDM in hardware. In future wok the proposed system could be improved so as to become a multi-user MIMO(MU-MIMO). [ABSTRACT FROM AUTHOR]

Using the orthogonal frequency division multiplexing (OFDM) technique, a multiple-input multiple-output (MIMO) system can provide high spectral efficiency and high data transmission over a fading channel. To achieve the best performance in a MIMO-OFDM system, high accuracy in channel estimation is a very important factor which leads to appropriate receiver design. Therefore, in the most channel estimation algorithms, the mean square error (MSE) is the main criterion for noise minimisation which is robust in the case of Gaussian noise. However, in telecommunication systems which do not have noise with Gaussian distribution, the MSE criterion is not appropriate. So as to tackle this problem, a robust adaptive filtering algorithm was proposed using minimum error entropy (MEE) criterion and improved least square (ILS) approach which by far is better than MMSE criterion to robust channel estimation. In this work, impulsive noise is used for non-Gaussian environment simulation. Furthermore, MEE is employed to attenuate the impulsive noise, and ILS is adopted to reduce LS estimation variance. Compared to the MSE-based algorithms, simulation results indicate that the proposed method outperforms the channel estimation in a non-Gaussian environment. [ABSTRACT FROM AUTHOR]

The coal mine internet of things (IoT) communication system is used for real-time monitoring of mining production to ensure the safety and reliability of personnel and equipment in the mine. To eliminate multipath fading in the process of wireless communication in mines, multiple-output multiplexing (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies are introduced. In this paper, a wireless communication system architecture of IoT in mining based on MIMO-OFDM is constructed. Aiming to solve the problems of intersymbol interference and frequency selective fading at the receiver, an improved minimum mean square error ordered successive interferences cancellation (MMSE-OSIC) signal detection algorithm is proposed. First, the signal-to-interference plus noise ratio of the received signal is calculated and the calculation results are sorted. The lowest signal-to-noise ratio is selected as the weakest signal layer. Then, the MMSE-OSIC algorithm is used to extract all of the signals, except the weakest layer. Finally, a maximum likelihood (ML) algorithm is used to traverse the whole signal domain; the signal symbol with the smallest distance from the weakest signal layer is found as the original signal of the weakest signal layer, and it is combined with the signal detected by MMSE-OSIC; then, the final signal detection result is obtained. The simulation results show that, compared with three benchmark algorithms, the proposed MMSE-OSIC algorithm has better signal detection performance under the conditions of different modulation methods and different channel numbers.

Abstract Multiple‐Input Multiple‐Output Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) is an essential technology in the wireless communication system. However, its performance is significantly affected by Carrier Frequency Offset (CFO), which leads to inter‐carrier interference (ICI). The CFO estimation and compensation techniques are used to mitigate the Doppler effect. In addition, the confidentiality of data is a key factor of the system. This paper proposes a Turbo‐based encryption and coding scheme to improve reliability and security for the MIMO‐OFDM wireless communication systems affected by the Doppler frequency offset. In our proposed scheme, the secret key is produced from channel parameters between legitimate users and it is used as a seed for generating a pseudo‐random bit sequence using an Advanced Encryption Standard (AES) generator with variable key lengths. The Turbo code's puncturing mechanism is controlled by this pseudo‐random bit sequence. The proposed method is applied to the system with different modulation schemes through the Rayleigh channel. The simulation results show that the system affected by the Doppler effect would obtain the best performance when using the highest key length of the AES generator, the BPSK, and a great number of receive antennas. Furthermore, the proposed solution outperforms several previous approaches for the MIMO‐OFDM systems.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 ×105 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods.

Multiple Input Multiple Output (MIMO) system with orthogonal frequency division multiplexing (OFDM) is a promising physical layer candidate for the current and future wireless standards. MIMO increases system throughput while OFDM maximizes spectrum efficiency. To reduce the impacts of frequency selective fading and increase the capacity, MIMO requires precise channel state information (CSI). The presence of carrier frequency offset (CFO) introduces intercarrier interference (ICI) among the subcarriers which degrades the system performance. MIMO channel estimation is done under the assumption of perfect frequency synchronisation between the transmitter and receiver, or zero CFO. However, such an assumption is incorrect for time-varying and frequency-selective wireless channels, and the presence of CFO has an impact on channel estimation accuracy. In the existing methods, CFO and channel estimations are carried out independently using separate training sequences which reduces the spectral efficiency. As a result, the pilot aided interpolation approach is proposed in this paper for the joint estimate and compensation of CFO and channel effects using the same set of pilot tones. Theresults show that the proposed method offers precise CFO and channel estimation which improves the bit error rate performance and carrier to interference power ratio (CIR) of MIMO-OFDM system. [ABSTRACT FROM AUTHOR]

The coal mine internet of things (IoT) communication system is used for real-time monitoring of mining production to ensure the safety and reliability of personnel and equipment in the mine. To eliminate multipath fading in the process of wireless communication in mines, multiple-output multiplexing (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies are introduced. In this paper, a wireless communication system architecture of IoT in mining based on MIMO-OFDM is constructed. Aiming to solve the problems of intersymbol interference and frequency selective fading at the receiver, an improved minimum mean square error ordered successive interferences cancellation (MMSE-OSIC) signal detection algorithm is proposed. First, the signal-to-interference plus noise ratio of the received signal is calculated and the calculation results are sorted. The lowest signal-to-noise ratio is selected as the weakest signal layer. Then, the MMSE-OSIC algorithm is used to extract all of the signals, except the weakest layer. Finally, a maximum likelihood (ML) algorithm is used to traverse the whole signal domain; the signal symbol with the smallest distance from the weakest signal layer is found as the original signal of the weakest signal layer, and it is combined with the signal detected by MMSE-OSIC; then, the final signal detection result is obtained. The simulation results show that, compared with three benchmark algorithms, the proposed MMSE-OSIC algorithm has better signal detection performance under the conditions of different modulation methods and different channel numbers. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication networks are based on vehicles that wirelessly exchange data, traffic congestion, and safety warnings between them. The design of new V2V systems requires increasingly energetically and spectrally efficient systems. Conventional multiple-input–multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have been used successfully for the last decade. However, MIMO-OFDM systems need to be improved to face future communication networks in high-mobility environments. This article proposes an efficient index modulation (IM)-based MIMO-OFDM system for V2V channels. The proposed transmission system is evaluated in high Doppler-spread channels. The results demonstrate that the proposed scheme reduces the required computational complexity in data detection and exhibits gains of up to 3 dB in bit error rate (BER) performance when compared to the conventional MIMO-OFDM system under the same conditions and parameters, along with achieving superior spectral efficiency. The results show the viability of implementing the proposed system in practical applications for high-transmission-rate V2V channels. [ABSTRACT FROM AUTHOR]

Nowadays, the Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) is an important method used in wireless communications, especially in 5G cellular communications. As in a wireless network, the input signals pass through a channel, and the input signal undergoes phase shift, attenuation, and interference. So, the information from the user side and the received signals differ. Thus, an effective channel estimator is essential to make cellular communication better. Hence, a novel hybrid technique called Chimp-based CatBoost channel estimation (CbCBCE) was proposed. This technique combines the Chimp optimization algorithm and the CatBoost algorithm. The channel parameters are estimated and then reduced using the Chimp optimization algorithm. Finally, the proposed model is validated with the case study. Then, the result of the proposed model was estimated and compared with other existing techniques. It is observed that the outcome of the proposed design is high compared to the other conventional methods. The presented model is executed in the MATLAB platform, proving that the proposed model has high throughput, increased energy efficiency, less BER, and a high data transfer rate. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The upcoming 5G communication is committed to providing ultra-high throughput and ultra-low delay service. However, digital signal processing technologies will be a critical challenge with the increasing bandwidth and transmitting streams. Especially, traditional fast Fourier transform (FFT) architectures are hard to meet the high-speed, high-performance, and low-overhead requirements. To overcome this issue, this article proposes a reconfigurable hyper-parallel bit-serial (HPBS) FFT processor. A bit-serial paradigm implements the datapath to reduce the hardware cost due to high parallelism as high as 64 to support FFT size from 64 to 2048. The HPBS design brings several significant advantages. Firstly, the hyper-parallel architecture supports large FTT radix with lower computation complexity, e.g., fewer processing stages and constant multipliers than conventional multipliers. Secondly, the bit-serial design enables the hyper-parallel FFT implemented by an acceptable hardware cost. Thirdly, the HPBS design supports the flexible and dynamic adjusting between latency and precision, which provides an additional optimization dimension. Based on the 55-nm process, the proposed 2048-point FFT can provide 32.128 Gbps throughput at 502MHz frequency with an average 12-bit word length. Besides, the normalized hardware efficiency can be up to 46.630 Gbps/mm2, which is at least 2X that of the traditional design.

Wireless channel estimation is one of the challenging problems in multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) wireless systems. The MIMO-OFDM exploits the spatial resources and increases the reliability and capacity of wireless systems. However, the performance of these systems depends on accurate channel estimation since the receivers require perfect channel state information (CSI) for coherent signal detection. Thus, wireless channel estimation is a necessary component of OFDM systems. In this article, we have proposed an algorithm for MIMO-OFDM systems that combines pilot symbols with reliable data symbols for channel estimation. The reliable data symbols serve as virtual pilots and enhance the spectral efficiency of the system. The proposed data aided channel estimation (DACE) algorithm eliminates the requirement of any additional resources such as excessive number of training sequences to attain the desired performance. Also, it outperforms the traditional least square (LS) and linear minimum mean square error (LMMSE) methods for channel estimation in terms of mean square error (MSE) and bit error rate (BER) performance of the system.

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), confirmed as an efficient technique, has been applied in optical wireless communications (OWCs) to improve the transmission rate. However, the disadvantage of high peak-to-average power ratio (PAPR) also brings into the OWC systems, especially for multi-user communications, resulting in low power efficiency. This paper proposes beam domain MIMO-OFDM OWCs with PAPR reduction. We describe the MIMO-OFDM OWC system architecture and derive the achievable sum rate and PAPR for multi-user OFDM systems, which are dominated by the precoding vectors. To reduce the PAPR, we design the precoding vectors to maximize the sum rate under the PAPR constraint. We propose an iterative process to obtain a candidate optimal solution by the sequential parametric convex approximation (SPCA) method. Furthermore, we prove that the PAPR increases with the number of users and propose a low complexity beam scheduling and power allocation algorithm. Numerical results validate that the proposed schemes can efficiently improve the transmission rate and reduce the PAPR.

MIMO OFDM is the key technology of 4G network system. MIMO-OFDM system enhances the spectrum efficiency and increases the capacity of the system. The implementation of USRP hardware to MIMO OFDM system has been attracted some researchers to conduct the experiments. So we conduct the experiments in a MIMO OFDM system that applies the predistortion technique. In this experiment, we evaluate performances of the predistortion technique by using the artificial neural network. USRP 2920 hardware which is supported by LabVIEW and Phyton software are used in this experiment. OFDM system uses 128 subcarriers to produce an OFDM symbol, and MIMO system uses 2 antennas at transmitter and receiver side. And no obstacles between Tx and Rx, or line of sight transmission scenarios. The performances of the predistortion technique using the artificial neural network algorithm are shown in symbol constellations or Error Vector Magnitude (EVM) at the receiver. And the texts or characters are used as the input of the system. From the experiment results can be seen that the distance between Tx and Rx affects the Error Vector Magnitude (EVM) and predistortion technique produces the Error vector magnitude (EVM) improvement. More shorter the distance between Tx and Rx can decrease distortions of the received signal, At the transmitter side, the performance of predistortion technique is shown as the linearization improvement of the non-linearity power amplifier. Therefore more wider the linear region of power amplifier results the decreasing in band distortion of transmitted signal, and can be seen as the Error Vector Magnitude (EVM) improvement.

Abstract The slice-wise multiplication of two tensors is required in a variety of tensor decompositions (including PARAFAC2 and PARATUCK2) and is encountered in many applications, including the analysis of multidimensional biomedical data (EEG, MEG, etc.) or multi-carrier multiple-input multiple-output (MIMO) systems. In this paper, we propose a new tensor representation that is not based on a slice-wise (matrix) description, but can be represented by a double contraction of two tensors. Such a double contraction of two tensors can be efficiently calculated via generalized unfoldings. It leads to new tensor models of the investigated system that do not depend on the chosen unfolding (in contrast to matrix models) and reveal the tensor structure of the data model, such that all possible unfoldings can be seen at the same time. As an example, we apply this new concept to the design of new receivers for multi-carrier MIMO systems in wireless communications. In particular, we consider MIMO-orthogonal frequency division multiplexing (OFDM) systems with and without Khatri–Rao coding. The proposed receivers exploit the channel correlation between adjacent subcarriers, require the same amount of training symbols as traditional OFDM techniques, but have an improved performance in terms of the symbol error rate. Furthermore, we show that the spectral efficiency of the Khatri–Rao-coded MIMO-OFDM can be increased by introducing cross-coding such that the “coding matrix” also contains useful information symbols. Considering this transmission technique, we derive a tensor model and two types of receivers for cross-coded MIMO-OFDM systems using the double contraction of two tensors.

Summary: The MIMO‐OFDM increases spectral efficiency without the use of channel state information (CSI). However, the bit error rate (BER) in the MIMO‐OFDM model increases dramatically due to the increase in nonlinear distortion in the power amplifier (PA). In this research, a novel pre‐coder integrated MIMO‐OFDM technology is designed for effective BER reduction, and its performance is analyzed. The discrete wavelet‐based OFDM system is one of the key ideas to present good orthogonal by emphasizing orthogonal wavelets. It helps to mitigate inter‐symbol interference (ISI) and noise within the channel by extending the bandwidth (BW) compared to conventional OFDM systems. But, DWT‐OFDM highly suffered due to downsampling, which degrades system efficiency. To overcome this, a redundant discrete wavelet transform (RDWT) is proposed in this article to enhance spectral efficiency. For modulation, 16‐bit quadrature amplitude modulation (QAM) is introduced in this work. In addition to this, a block bi‐diagonalization (BBD) pre‐coder technique is proposed to mitigate unwanted noise and interference in the MIMO‐OFDM system. The performance measures such as ergodic sum capacity, BER, throughput, average jitter noise power, mean square error (MSE), and power consumption are analyzed using the MATLAB platform. The proposed method obtains the ergodic sum capacity of 6.25 bps/Hz, a BER of 0.021, a throughput of 95.2%, MSE of −0.47, and a power consumption of 1.15 μW. The simulative results prove the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Multiple‐Input Multiple‐Output Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) is an essential technology in the wireless communication system. However, its performance is significantly affected by Carrier Frequency Offset (CFO), which leads to inter‐carrier interference (ICI). The CFO estimation and compensation techniques are used to mitigate the Doppler effect. In addition, the confidentiality of data is a key factor of the system. This paper proposes a Turbo‐based encryption and coding scheme to improve reliability and security for the MIMO‐OFDM wireless communication systems affected by the Doppler frequency offset. In our proposed scheme, the secret key is produced from channel parameters between legitimate users and it is used as a seed for generating a pseudo‐random bit sequence using an Advanced Encryption Standard (AES) generator with variable key lengths. The Turbo code's puncturing mechanism is controlled by this pseudo‐random bit sequence. The proposed method is applied to the system with different modulation schemes through the Rayleigh channel. The simulation results show that the system affected by the Doppler effect would obtain the best performance when using the highest key length of the AES generator, the BPSK, and a great number of receive antennas. Furthermore, the proposed solution outperforms several previous approaches for the MIMO‐OFDM systems. [ABSTRACT FROM AUTHOR]

In recent years, the development of communication technology has advanced at an accelerated rate. Communication technologies such as 4G, 5G, Wi-Fi 5 (802.11ac), and Wi-Fi 6 (802.11ax) are extensively used today due to their excellent system quality and extremely high data transfer rates. Some of these technologies incorporate MIMO-OFDM into their protocol. MIMO-OFDM is widely used in modern communication systems due to its benefits, which include high data rates, spectral efficiency, and fading resistance. Despite these benefits, MIMO-OFDM has disadvantages, with the use of a nonlinear HPA being one of them. Nonlinear HPA causes in-band and out-of-band distortions in MIMO-OFDM signals. Utilizing predistortion (PD) is one way of solving this issue. PD is a technique that uses the inverse distortion of the HPA to compensate for the nonlinear characteristics of the HPA. To enhance the quality of MIMO-OFDM systems that the use of HPA has degraded, the convolutional coding (CC) method can be combined with the help of PD. Convolutional coding is a type of channel coding that can be used for error detection and correction. This study will evaluate a combined technique of PD neural networks (PDNN) and CC on the MIMO-OFDM system using Software Defined Radio (SDR) devices. The evaluation of this system led to the use of a technique that combines PDNN and CC to improve SNR and minimise BER on MIMO-OFDM systems that HPA on SDR devices has degraded. In addition, at code rates 1/2, 2/3, and 3/4, using PDNN reduces the SNR value required to achieve BER equal to 0 by 12.037%, 37.8%, and 4.10% when compared to Digital Predistortion (DPD).

Many studies have been published to address the growing issues in wireless communication systems. Space-Time Block Coding (STBC) is an effective and practical MIMO-OFDM application that can address such issues. It is a powerful tool for increasing wireless performance by coding data symbols and transmitting diversity using several antennas. The most significant challenge is to recover the transmitted signal through a time-varying multipath fading channel and obtain a precise channel estimation to recover the transmitted information symbols. This work considers different pilot patterns for channel estimation and equalization in MIMO-OFDM systems. The pilot patterns fall under two general types: comb and block types, with a proper arrangement suitable to the multiple transmit antennas. The two main channel estimation methods, LS and MMSE, are compared by evaluating performance in terms of Bit Error Rate (BER) to analyze the performance of pilot-aided channel estimation for 2x2 and 4x4 MIMO arrangements utilizing LTE parameters and the effects of modifying different numbers of OFDM subcarriers under different channel models It has been discussed, a 4x4 system performs better than a 2x2 system in terms of BER with an acceptable amount of additional complexity.

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.

MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.

With each passing year, there is a rapid increase in mobile data traffic and also there occurs a demand for bringing a higher data rate to a growing number of users. The next generation networks need to depend on denser infrastructure deployment, reduced inter- and intra-cell interference, [2] simple signal processing, and reduced transmitted power, as well as improved energy and spectral efficiency, to provide seamless connectivity. The mathematical modeling of MIMO systems is discussed, with emphasis on the technology's key features. The latest developments in the MIMO area, such as Multi-user MIMO (MU-MIMO) [1], Massive MIMO, and MIMO OFDM techniques, are then discussed in detail, with an emphasis on their importance in cellular communication systems. Massive MIMO has been regarded as the best approaches to increase spectral and energy efficiency in broadband wireless communication systems. The massive MIMO's channel model must be explored as the first step in evaluating the performance of any communication system. [ABSTRACT FROM AUTHOR]

Among numerous wireless communication networks, MIMO-OFDM plays a vital role in the enhanced data transmission rate. Numerous researches are performed based on estimating the channel to attain optimal output. However, because of the enhanced bit error rate, attaining an optimal estimated channel is a challenging task. So to decrease the bit error rate and to enhance the system performances, this article aims in proposing the mutable weight-based adaptive particle swarm optimization (MAPSO) based MIMO-OFDM approach for the optimal channel estimation. This approach consists of four phases: OFDM with MIMO, STBC based OFDM coding, signal recognition, and MC-CDMA for the evaluation of optimal channel estimation. The MAPSO algorithm is used in the MC-CDMA approach for tuning the parameters at every level of channel estimation to achieve optimal value. The metrics such as SER, BER, MSE, and NMSE are computed for the proposed approach to achieve better performance. The comparative analysis is carried out with several other existing approaches and the experimental results reveal that the proposed MAPSO based MIMO-OFDM approach outperformed other approaches. [ABSTRACT FROM AUTHOR]

In this paper, channel overhead is reduced by exploiting channel sparsity for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. Where, compressive sensing (CS) based dictionary design algorithms has been adopted as a channel estimation technique in high mobile systems with minimal number of pilots, such as high-speed train (HST) systems. A novel framework design of the dictionary-based CS was proposed considering both delay and Doppler effects in order to correctly recover the channel response. The channel under consideration is a 2 by 2 space-time block code (STBC) MIMO channel. Simulation tests according to the international telecommunication union (ITU) channel model demonstrated the suitability of the proposed dictionary for estimating the channel impulse response (CIR) of a liner time varying (LTV) channel with a mobility approaches 675 Km/h related to a Doppler frequency of 1500 Hz and 2.4 GHz carrier frequency. Two CS recovery algorithms were applied; orthogonal matching pursuit (OMP) and basis pursuit (BP), where by about 7 dB gain in signal to noise ratio (SNR) was achieved with mobility of 675 Km/h using OMP as compared to BP at a bit error rate (BER) of 10-3 with 128 OFDM subcarriers. [ABSTRACT FROM AUTHOR]