DFT-Based Channel Estimation with Channel Response Mirroring for MIMO OFDM Systems

In this paper, DFT-Based channel estimation with channel response mirroring is proposed and analyzed. In General, pilot symbols for channel estimation in MIMO(Multi-Input Multi-Output) OFDM(Orthogonal Frequency-Division Multiplexing) Systems have a diamond shape in the time-frequency plane. An interpolation technique to estimate the channel response of sub-carriers between reference symbols is needed. Various interpolation techniques such as linear interpolation, low-pass filtering interpolation, cubic interpolation and DFT interpolation are employed to estimate the non-pilot sub-carriers. In this paper, we investigate the conventional DFT-based channel estimation for noise reduction and channel response interpolation. The conventional method has performance degradation by distortion called “edge effect” or “border effect”. In order to mitigate the distortion, we propose an improved DFT-based channel estimation with channel response mirroring. This technique can efficiently mitigate the distortion caused by the DFT of channel response discontinuity. Simulation results show that the proposed method has better performance than the conventional DFT-based channel estimation in terms of MSE.

Implementation of Sparse Techniques for MIMO Channel Estimation

Channel estimation plays a very important role on the performance of wireless communication systems.Channel estimation can be carried out in different ways: with or without the help of a parametric model, using frequency and/or time correlation properties in the wireless channel, blind methods or those based on training pilots, adaptive or non-adaptive methods. As more antennas are added to Multiple Input Multiple Output (MIMO) system more computational resources and power are required. There is a need to address this problem of the overhead in training symbol based models for channel estimation. Compressed Sensing (CS) algorithms are beneficial in addressing these limitations in the system to increase the spectral efficiency can help free up resources and prevent additional taxing on the hardware. Compressed sensing the pilot symbols by using CS algorithms like OMP, SP, and CoSaMP algorithms. The channel coefficients are obtained through LS, MMSE and LMS techniques. Then, a very small amount of frequency-domain orthogonal pilots are used for the accurate channel estimation.The traditional algorithms like LS , MMSE and LMS combined with CS algorithms have better performance at low SNRs compared to conventional techniques alone in terms of computational time complexity and Normalised Mean Square Error(NMSE) performance. There is a halving of pilot symbols used for training by using the CS algorithm. MSE performance is increased with increase in sparsity level. CoSaMP performs better than SP and OMP at low SNRs. With increase in sparsity level after 50K, the performance of SP is comparable to that of CoSaMP. OMP is simple to implement but MSE performance is less and computational time is more compared to SP and CoSaMP.

Autoencoder-bank based design for adaptive channel-blind robust transmission

The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there exists an explicit channel model for training that matches the actual channel model in the online transmission. The variation of the actual channel indeed imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. Specifically, we partition channel coefficient values into sub-intervals, train an AE for each partition in the offline phase, and constitute a bank of AEs. Then, based on the actual channel condition in the online phase and the average block error rate (BLER), the optimal pair of encoder and decoder is selected for data transmission. To gain knowledge about the actual channel conditions, we assume a realistic scenario in which the instantaneous channel is not known, and propose to blindly estimate it at the Rx, i.e., without any pilot symbols. Our simulation results confirm the superiority of the proposed adaptive scheme over existing methods in terms of the average power consumption. For instance, when the target average BLER is equal to $$10^{-4}$$ 10 - 4 , our proposed algorithm with 5 pairs of AE can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme.

Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application

In this work, discrete Fourier transform (DFT)-based channel estimation is proposed in generalized frequency division multiplexing (GFDM) system. In the GFDM system, the subcarriers are non-orthogonal; therefore, the pilot symbols cannot be easily observed due to the interference from data symbols and noise. The proposed method can improve the channel estimation of least square (LS) method by eliminating channel impulse response outside the number of actual impulse response. First, the received signal is demodulated using zero forcing demodulator. Then, it is divided with transmitted pilot symbols to obtain channel response. Interpolation in frequency and time domains is conducted to acquire channel response for all GFDM blocks. Finally, the channel estimation algorithm using DFT is performed. The parameters of the system are adjusted so that they are suitable for tactile internet application. The channel model used is NYUSIM, which utilizes mmWave. Three scenarios in NYUSIM such as urban microcell, urban macro cell and rural macro cell are used and power delay profiles generated from NYUSIM simulator are employed in this system. The results show that mean square error (MSE) from DFT-based channel estimation gives substantial improvement for all scenarios. In addition, symbol error rate (SER) of DFT-based channel estimation provides a slight improvement of 1.5 dB than LS channel estimation.

A New Robust Decoding Technique of Four Transmitters MIMO STBC System based on FastICA Algorithm

A new robust decoding technique which designed of Multiple-Input Multiple-Output Space–Time Block Code (MIMO-STBC) using Fast Independent Component Analysis (Fast-ICA) based on proposed mixing model has been performed in this paper. This decoding technique is characterized by i) complexity is very low, ii) the speed is high and iii) BER performance is excellent. It can be achieved with any MIMO STBC system with a fewer pilot symbols number. Also, it is reduced decoding time into 1/8 by innovating a simple strategy referred by one source extraction method. Also, this paper includes suitable initializing for the de-mixing vector to solve the ambiguities problem of sign and source of blind source separation (BSS). To test the proposed technique, four transmitters (4Tx) STBC MIMO system was implemented using MATLAB2018. It also found that excellent BER performance associated with a high number of symbols per frame (about 8012 symbols). The simulation results show that the new decoder works for any number of receiver antenna (Nr = 2, 4 and 5). As compare with classical decoding algorithm, it is found that the new decoder provides coding gain (at BER =10-6 ) equal to 1 dB,1.45 dB and 1.76 dB when Nr = 2,4 and 8 respectively, using only 2~3 iterations only.

Autoencoder-bank Based Design for Adaptive Channel-Blind Robust Transmission

The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there is an explicit channel model for training that matches the actual channel model in the online transmission. Since the actual channel varies over time, this imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. More precisely, we divide the interval of random channel coefficients into n sub-intervals. Subsequently, in the offline training phase, we employ an AE bank consisting of n pairs of encoder and decoder and perform training over the sub-intervals. Then, in the online transmission phase, based on the actual channel conditions, the optimal pair of encoder and decoder is selected for data transmission in terms of satisfying an average block error rate (BLER) constraint imposed on the system. To monitor actual channel conditions for adopting the adaptive scheme, we assume a realistic scenario where the instantaneous channel gain is not known to Tx/Rx and it is blindly estimated at the RX, i.e., without using any pilot symbols. Our simulation results confirms the superiority of the proposed adaptive scheme over a non-adaptive scenario in terms of average power consumption. For instance, when the target average BLER is equal to 10−4 , our proposed algorithm with n = 5 can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme

Autoencoder-Bank Based Design for Adaptive Channel-Blind Robust Transmission

The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there is an explicit channel model for training that matches the actual channel model in the online transmission. Since the actual channel varies over time, this imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. More precisely, we divide the interval of random channel coefficients into n sub-intervals. Subsequently, in the offline training phase, we employ an AE bank consisting of n pairs of encoder and decoder and perform training over the sub-intervals. Then, in the online transmission phase, based on the actual channel conditions, the optimal pair of encoder and decoder is selected for data transmission in terms of satisfying an average block error rate (BLER) constraint imposed on the system. To monitor actual channel conditions for adopting the adaptive scheme, we assume a realistic scenario where the instantaneous channel gain is not known to Tx/Rx and it is blindly estimated at the RX, i.e., without using any pilot symbols. Our simulation results confirms the superiority of the proposed adaptive scheme over a non-adaptive scenario in terms of average power consumption. For instance, when the target average BLER is equal to 10−4, our proposed algorithm with n = 5 can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme.

COMMUNICATION SYSTEMS FOR HIGH SPEED FLYING DEVICES WITH REPETITION CODES

Communication systems for devices moving at high speed are suffering from error-floor due to the Doppler effect. This paper proposes a simple narrowband communication systems for high speed flying devices for critical applications such as missile and drone. To make the system simple, we consider Repetition codes and slight increase of the number of pilot symbols such that the system can predict accurately the fast-changing channel due to time-selective fading. The equalizer in this paper is designed according to the addition of the pilot symbols so that the system works at a maximum speed of 450 km/h to make successful operation for missile and drone before they are taken down by the enemy. Computer simulations are used to evaluate the performance of the proposed communication systems. The operating frequency is industrial, scientific, and medical (ISM) band, where binary phase shift keying (BPSK) modulations are used with Repetition codes being the channel coding. The bit error rate (BER) performance is evaluated under additive white Gaussian noise (AWGN) and Rayleigh fading channels. The results confirm that excellent BER performances are obtained having error-floor less than 10????4 making many applications, including image transmission, are possible, which are great for high speed flying devices even with Repetition codes and simple zero forcing (ZF) equalizer. The results of this study are expected to help the development of future communication systems for missile, drone, and airplane applications.