Channel estimation analysis in MIMO-OFDM wireless systems

A multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency partition multiplexing (OFDM) modulation technique can achieve reliable high data rate show over broadband wireless channels.Channel state information for both single-input single-output (SISO) and MIMO systems based on pilot aided arrangement is investigated in this paper.The estimate of channel at pilot frequencies with conventional Least Square (LS) and Minimum Mean Square (MMSE) estimation algorithms is carried out through Matlab simulation.For outdoor communicationscenarios, where wireless channels are sparse in nature, pathinterruptions of different transmit-receive antenna couples share a jointsparse decoration due to the spatial correlation of MIMO channels.By simultaneously exploiting those MIMOchannel characteristics, the proposed scheme performs better thanpresent state-of-the-art systems.Furthermore, by joint processingof signals associated with different antennas, the pilot overheadcan be reduced the performance of MIMO OFDM and SISO OFDM are assessed on the basis of Bit Error Rate (BER) and Mean Square Error (MSE) level.Further enhancement of presentation can be attained through maximum diversity Space Time Block Coding and Maximum Likelihood Detection at transmission and reception ends respectively.MMSE estimation has been shown to perform much better than LS but is more complex than LS for the MIMO system using pilot carriers.

Recently, technological developments have enhanced, the use of Millimeter-wave (mm-Wave) Multiple Input Multiple Output (MIMO) system in various communication applications and wireless sensor networks as channel estimation efficiency can be immensely improved with the help of this technological developments in Millimeter-wave MIMO system and wireless sensor network as well. Moreover, they can improve quality of communication services to a great extent. However, cell interference in Millimeter-wave (mm-Wave) MIMO system can produce a massive impact on spectral efficiency. Therefore, a Routing Enabled Channel Estimation (RECE) technique is presented in this article to minimize interference between cells. The proposed Channel Estimation technique improves channel capacity as well as spectral efficiency. Moreover, Normalized Mean Square Error (NMSE) is minimized heavily using proposed RECE technique. Here, main aim of this article is to reduce cell interference and channel estimation inside a cell by using route selection, beam selection, and spatial frequency estimation. Here, different scenarios and parameters are considered to evaluate performance efficiency of proposed RECE technique in terms of spectral efficiency, NMSE and SNR and compared against varied traditional channel estimation techniques. Moreover, it is clearly evident from performance results that the proposed channel estimation technique performs better than the other two methods.

This paper introduces four block joint detection (JD) receivers which combining the orthogonal frequency division multiplexing (OFDM) and code division multiple access (CDMA) techniques, i.e., OFDM-CDMA receiver. The performance of the OFDM-CDMA receiver with orthogonal and non-orthogonal spreading sequences is investigated in mobile radio channels. The sequences considered as spreading codes are orthogonal Walsh-Hadamard, Orthogonal Gold, Gold and maximim length sequences or m - sequences. The JD algorithms for the OFDM- CDMA receiver are Zero Forcing Block Linear Equalizer (ZF- BLE), Minimum Mean Square Error Block Linear Equalizer (MMSE-BLE), with and without decision feedback (DF). Time- domain equalization does not require DFT or FFT operations and can be used to compensate the channel fading effect. Hence for hardware implementation, the time domain interpretation can lead to a more efficient structure when compared with the frequency domain approach. The bit error rate (BER) performance comparisons of different spreading codes and equalizers are shown in the simulations.

This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems, also known as “massive MIMO”. Unlike previous works on this topic, which mainly considered the impact of inter-cell disturbance due to pilot reuse (so-called pilot contamination), we are concerned with the computational complexity. The conventional minimum mean square error (MMSE) and minimum variance unbiased (MVU) channel estimators rely on inverting covariance matrices, which has cubic complexity in the multiplication of number of antennas at each side. Since this is extremely expensive when there are hundreds of antennas, we propose to approximate the inversion by an L-order matrix polynomial. A set of low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced. The coefficients of the polynomials are optimized to yield small mean square error (MSE). We show numerically that near-optimal performance is achieved with low polynomial orders. In practice, the order L can be selected to balance between complexity and MSE. Interestingly, pilot contamination is beneficial to the PEACH estimators in the sense that smaller L can be used to achieve near-optimal MSEs.

Channel estimation (CE) is a challenging problem and one of the key technologies in Orthogonal Frequency Division Multiplexing (OFDM) systems. The Linear Minimum Mean Square Error (LMMSE) proposed for OFDM systems has excellent Mean Square Error (MSE) performance. However, the conventional method requires the statistical knowledge of the channel information in advance and the complicated inverse operation of large dimension matrices. In this case, an enhanced fast LMMSE channel estimation using Fast Fourier Transform (FFT) operation and the most significant taps (MST) algorithm for OFDM systems is proposed. This method has the advantage of low computational complexity and no requirement on the detailed channel information. In addition, simulation results demonstrate that the proposed algorithm outperforms other LMMSE methods in terms of bit error rate (BER) without the loss of MSE performance.

Rapidly growing demand for high data rates in wireless communication systems is the cause of the merging of Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) techniques. MIMO-OFDM technique needs Radio Frequency (RF) front end in each antenna, which is not effective for large dimensional antenna size. Therefore, RF front end on the receiver side will be simplified using a single RF technique based on the Electrically Steerable Parasitic Array Radiator (ESPAR) antenna. In real life, a channel cannot be ideal. Therefore, before the signal is transmitted, that signal must be coded to minimized errors when the data transmission process. Convolutional code is a coding technique that can detect and correct error bit (error control coding) and that it is expected to reduce the quantity of Bit Error Rate (BER). In this research, a simulation is designed to analyze single RF performance on the MIMO-OFDM system using Vertical Bell Laboratories Space-Time (VBLAST) detection for convolutional code encoder and Viterbi decoder. The result simulation shows that the channel estimation technique works well on the MIMO-OFDM system using single RF, which is shown in the magnitude and phase curves against the subcarrier index between the theory channel and the estimated channel are at almost the same points. VBLAST-ZF detector performance is shown in the BER curve against SNR, where the difference BER value for SNR value 10 dB between the theory channel and the estimated channel is 0.001499. Channel coding technique using convolutional code encoder and Viterbi decoder with a rate of ½ works well on the MIMO-OFDM system using single RF which is shown in the BER curve against SNR, where BER value with coding technique is smaller than BER value without coding technique.

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique. It supports high data rates over wireless radio channels. Due to high peak-to-average power ratio, OFDM is sensitive to the non-linear effects of the power amplifier. So, many methods of PAPR reduction techniques have been proposed. Of these, the distortionless method of PAPR reduction is Partial Transmit Sequence (PTS) technique. But this method suffers from high computational complexity. Modified PTS technique has been proposed to address the issue of high computational complexity. Modified PTS technique adopts a neighbourhood search algorithm to find the optimum set of phase factors. In this paper, the performance the OFDM system by using modified PTS technique combined with interleaving and pulse shaping is evaluated by employing Least Square (LS) and Minimum Mean Square Error (MMSE) channel estimation methods. Mean Square Error (MSE) and Bit error rate (BER) are the performance measures that are employed to analyse the performance of the OFDM system. The simulation result shows that the performance of OFDM system improves with PAPR reduction.

In this paper, the combination of orthogonal frequency-division multiplexed (OFDM), multi-input multi-output (MIMO) and adaptive modulation can effectively eliminate or resist various fading in wireless channels, thus it can improve the capacity of system and transmission quality. Depending on the optimal bit and power allocation strategy based on greedy algorithm, in this paper, the performance of adaptive bit and power allocation MIMO-OFDM system based on greedy algorithm is completely studied. And the performance comparison among greedy algorithm, Chow algorithm and average algorithm is represented. We also compare the performance of MIMO-OFDM system under different antennas numbers. It is shown that the bit error rate (BER) of MIMO-OFDM system based on greedy algorithm is better than that based on Chow algorithm and average algorithm. And the spectral efficiency of greedy algorithm is superior to that of Chow and average algorithm. Ultimately, we show that the performance of MIMO-OFDM system is also superior to that of OFDM system with single transmit antenna and receive antenna which is under the same scheme as the former.

A new approach to Semi–Blind Channel Estimation (SBCE) technique for Multiple Input Multiple Output (MIMO) wireless communication system is proposed in this paper. It combines the least squares (LS) and Minimum Mean Square Error (MMSE) Training–Based Channel Estimation (TBCE) scheme with whitening rotation–based SBCE scheme. In this approach, the singular value decomposition has to be applied only once, thereby reducing the computational complexity. The channel correlation matrix required by MMSE technique can be obtained based on latest channel conditions. The performance of this scheme is compared with whitening rotation–based SBCE under various parameters such as modulation schemes, Signal–to–Noise Ratio (SNR) and frame length. The performance of the new approach is found to be better in terms of Bit Error Rate (BER) for high SNR, higher modulation order and smaller frame lengths. With low SNR, lower modulation order and larger frame lengths, there is negligible reduction in performance.

This paper presents a novel precoder design for an orthogonal frequency division multiplexing (OFDM) system using a channel estimator. First, an asymptotically tight approximation of the pairwise error probability (PEP) error with channel estimation error is presented and is shown to improve the existing upper bound of the PEP. Using the proposed approximation, a near-optimal power allocation scheme is derived and investigated and a new precoding scheme is introduced to improve the bit error rate (BER) performance of the receiver assisted by a minimum mean square error (MMSE) channel estimator. Both experimental and theoretical results included in this paper show improvement in the BER performance of a receiver with channel estimators utilizing the introduced precoder and power allocation scheme.

In this paper, we investigate the robustness of the Unique Word Orthogonal Frequency Division Multiplexing (UW-OFDM) technique for channel estimation issue. We consider the UW-OFDM technique instead of Zero-Padding (ZP) OFDM one to construct the OFDM symbols for MultiBand Orthogonal Frequency Division Multiplexing Ultra WideBand (MB-OFDM UWB) systems. Then, we propose a two-step LMMSE channel estimator. In the first step, by using the knowledge of UW sequence in the receiver side, the Channel Frequency Response (CFR) is estimated. In the second step, the Linear Minimum Mean-Squared Error (LMMSE) channel estimation is performed using the estimated CFR of the first step and the Channel Estimation Sequence (CES) included in the frame preamble. We evaluate the performance of the proposals in terms of Bit Error Rate (BER) by computer simulations. We show that the consideration of UW-OFDM technique provides, not only reduction of complexity, but also improvement in BER performance. Moreover, we show that the two-step LMMSE channel estimation does not affect the system performance mainly for low Signal-to-Noise Ratio (SNR).

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR.

Large scale multiple-input multiple-output (MIMO) or Massive MIMO is one of the pivotal technologies for future wireless networks. However, the performance of massive MIMO systems heavily relies on accurate channel estimation. While the acquisition of channel state information (CSI) in such systems requires an increasingly large amount of training overhead as the number of users grows. To tackle this issue, in this paper, we propose a deep learning assisted blind channel estimation technique for orthogonal frequency division multiplexing (OFDM) based massive MIMO systems. We prove that by exploiting the asymptotic orthogonality of the massive MIMO channels, the channel distortion can be averaged out without the prior knowledge of channel impulse responses, and after some mathematical manipulation, different users' transmitted data symbols can be extracted. Thus, by deploying a denoising convolutional neural network algorithm (DnCNN), we mitigate a remaining channel and noise effect to accurately detect the transmitted data symbols at the channel sounding stage. Using the detected data symbols as virtual pilots, we estimate the CSI of all the users at each BS antennas. Our simulation results testify the efficacy of our proposed technique and demonstrate that it can provide a mean square error (MSE) performance which coincides with that of the data-aided channel estimation technique.

Multipath channels often exhibit sparse structures in multicarrier underwater acoustic (MC-UWA) communication systems. Conventional linear channel estimators, such as least squares (LS) and minimum mean square error (MMSE) are considered as optimal solutions under the assumption of rich multipath. However, they often result in low spectral efficiency due to neglect of the sparsity in multipath channels. In this paper, we propose a novel sparse channel estimation method with compressive sensing. The proposed estimator has better estimation performance and low complexity when compared with the LS estimator. Simulations verified the proposed method with respect to the mean square error (MSE) and the computational complexity. Key words: Underwater acoustic (UWA), orthogonal frequency division multiplexing (OFDM), sparse multipath, compressive sensing (CS), sparse channel estimator.

In recent years, there has been an increased need for digital wireless applications to use high rate data transmission. OFDM (Orthogonal Frequency Division Multiplexing) offers an interesting solution that allows for the exploitation of the 60 GHz band with optimal spectral efficiency, a robustness to frequency selective fading, and a resistance to inter-symbol interference (ISI) that is a major problem in high speed data communications. Transmitted data in an OFDM system is divided on different subcarriers, after applying PSK (phase shift keying) or QAM (Quadrature Amplitude Modulation) modulation. The bandwidth of the obtained signal is converted to the time domain by using an IFFT (Inverse Fast Fourier Transform) in order to transmit it through a wireless channel. To recover the distorted data at the receiver, the effects of the channel must be estimated and compensated by the receiving system [1, 2]. In this paper, the 60 GHz channel estimation methods for OFDM systems based on comb-type pilot arrangement are investigated, as the algorithm of channel estimation based on comb-type is divided into pilot signal estimation and channel interpolation. The pilot signal estimation based on LS (Least Squares) or LMMSE (Linear Minimum Mean Square Error) criteria is studied along with the channel interpolation based on LI (linear interpolation). The performances of various estimation algorithms are evaluated and compared by measuring the Bit Error rate (BER) and Mean Square Error (MSE) where 16-QAM modulation scheme is applied.