Channel estimation algorithms for cooperative spectrum sensing in amplify‐and‐forward cooperative system

After a few decades’ evolution of wireless communication systems, to ensure reliable highspeed communication over unreliable wireless channels is still one of the major challenges facing researchers and engineers. The use of multiple antennas at transmitter and receiver, known as multiple-input multiple-output (MIMO) communications, is one promising technology delivering desired wireless services. The main goal of this thesis is to study two important issues in wireless MIMO communication systems: receiver design for coded MIMO systems, and diversity-multiplexing tradeoff analysis in general fading channels. In the first part of this thesis, we decompose the receiver design problem into two subproblems: MIMO channel estimation and MIMO detection. For the MIMO channel estimation, we develop an expectation-maximization (EM) based semi-blind channel and noise covariance matrix estimation algorithm for space-time coding systems under spatially correlated noise. By incorporating the proposed channel estimator into the iterative receiver structure, both the channel estimation and the error-control decoding are improved significantly. We also derive the modified Cramer-Rao bounds (MCRB) for the unknown parameters as the channel estimation performance metric, and demonstrate that the proposed channel estimation algorithm can achieve the MCRB after several iterations. For the MIMO detection, we propose a novel lowcomplexity MIMO detection algorithm, which has only cubic order computational complexity, but with near-optimal performance. For a 4×4 turbo-coded system, we show that the proposed detector had the same performance as the maximum a posteriori (MAP) detector for BPSK modulation, and 0.1 dB advantage over the approximated MAP detector (list sphere decoding algorithm) for 16-QAM modulation at BER = 10−4. In the second part of this thesis, we derive the optimal diversity-multiplexing tradeoff for

SummaryThis paper introduces the concept of spatial and media‐based modulated (SMBM) orthogonal frequency division multiplexing (OFDM) as a potential candidate for highly mobile next generation beyond 5G (B5G) wireless communications. The proposed SMBM‐OFDM technique utilizes not only the transmit antenna and channel state indices but also OFDM subcarriers to improve the system performance under high mobility. In addition, this study sheds light on challenging fast time‐varying channel estimation problem of MBM‐based systems by using the linear minimum mean square error (LMMSE) approach due to its optimality to investigate the achievable system performance with the aid of basis expansion modeling. The minimum lower bound on the channel estimation error (Bayesian Cramer–Rao bound) is derived theoretically and shown to be attainable by the considered LMMSE estimator. Moreover, symbol detection performance is provided for different modulation types and higher mobile velocities. Simulation results demonstrate that SMBM‐OFDM system under high mobility is able to provide around 12‐dB performance gains in terms of both channel estimation and symbol detection error compared to conventional spatial modulation (SM)‐OFDM systems without MBM. The presented framework is important due to addressing the high mobility support of SMBM‐OFDM systems for B5G wireless communications in terms of achievable channel estimation and data detection performance.

This paper, deals with H-infinity channel estimation technique for Orthogonal Frequency Division Multiplexing (OFDM) systems, in fading channel. Channel estimation is important for wireless applications because, in practical transmission scenario, channel correlation functions are either not known or cannot be easily estimated. It is therefore desirable to have an estimator which is robust to mismatches between the assumed and the actual channel correlation functions. The wireless channel has fading characteristics and its time varying nature adds additional cost to estimator design. A simplified Kalman filter is proposed which reduces the noise effects of the least square estimation. While the Kalman filtering algorithm requires the noise process to be zero mean besides the requirement to know the standard deviation of the noise process, H-infinity filtering algorithm makes no assumption about the noise and hence is said to be more robust in the wake of uncertainty. The proposed channel estimation algorithm is more robust in terms of model uncertainty and is more suitable for OFDM systems. Further, the complexity of the H-infinity filter is not too high. Given the conditions, H-infinity poses a suitable filter than any other, for channel estimation.

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.

The channel estimation technique is investigated in OFDM communication systems with multi-antenna Amplify-and-Forward (AF) relay. The Space-Time Block Code (STBC) is applied at the transmitter of the relay to obtain diversity gain. According to the transmission characteristics of OFDM symbols on multiple antennas, a pilot-aided Linear Minimum Mean-Square-Error (LMMSE) channel estimation algorithm with low complexity is designed. Simulation results show that, the proposed LMMSE estimator outperforms least-square estimator and approaches the optimal estimator without error in the performance of Symbol Error Ratio (SER) under several modulation modes, and has a good estimation effect in the realistic relay communication scenario.

Channel estimation is a critical component in orthogonal frequency division multiplexing (OFDM) systems for ensuring reliable wireless communication. In this study, we propose a fast super-resolution convolutional neural network (FSRCNN) model for channel estimation, designed to reduce computational complexity while maintaining high estimation accuracy. The proposed FSRCNN model incorporates modifications such as replacing linear interpolation with zero padding and leveraging a new fast CNN architecture to estimate channel coefficients. Our numerical experiments and simulations demonstrate that the FSRCNN model significantly outperforms traditional methods, such as least square (LS) and linear minimum mean square error (LMMSE), in terms of mean square error (MSE) across various signal-to-noise ratios (SNRs). Specifically, the FSRCNN model achieves MSE values comparable to MMSE estimation, particularly at higher SNRs, while maintaining lower computational complexity. At an SNR of 20 dB, the FSRCNN model shows a notable improvement in MSE performance compared to the ChannelNet and LS methods. The proposed model also demonstrates robust performance across different SNR levels, with optimal results observed when the training SNR is close to the operating SNR. These findings validate the effectiveness of the FSRCNN model in providing a low-complexity, high-accuracy alternative for channel estimation, making it suitable for real-time applications and devices with limited computational resources. This advancement holds significant promise for enhancing the reliability and efficiency of current and future wireless communication networks.

In orthogonal frequency division multiplexing (OFDM) systems, the least square (LS) in addition to the technique of linear minimum mean square error (LMMSE) channel estimation are compared using frequency as well as time domain in this paper. A constant LMMSE channel estimation has been proposed in time domain where the channel is not intimated before. It is presented by the Simulation outcomes that the channel estimation execution can be improved by the techniques of time domain but not using the techniques of frequency domain. Additionally, using the technique of LMMSE, higher accuracy is achieved by the proposed method which is presented in the outputs of the simulation.

Railway signaling is a safety system that has evolved over the last couple of centuries towards autonomous functionality. Recently, great effort is being devoted in this field, towards the use and exploitation of Global Navigation Satellite System (GNSS) signals and GNSS augmentation systems in view of lower railway track equipments and maintenance costs, that is a priority to sustain the investments for modernizing the local and regional lines most of which lack automatic train protection systems and are still manually operated. The objective of this paper is to assess the sensitivity of the Linear Minimum Mean Square Error (LMMSE) algorithm to modeling errors in the spatial correlation function that characterizes true pseudorange Differential Corrections (DCs). This study is inspired by the railway application; however, it applies to all transportation systems, including the road sector, that need to be complemented by an augmentation system in order to deliver accurate and reliable positioning with integrity specifications. A vector of noisy pseudorange DC measurements are simulated, assuming a Gauss-Markov model with a decay rate parameter inversely proportional to the correlation distance that exists between two points of a certain environment. The LMMSE algorithm is applied on this vector to estimate the true DC, and the estimation error is compared to the noise added during simulation. The results show that for large enough correlation distance to Reference Stations (RSs) distance separation ratio values, the LMMSE brings considerable advantage in terms of estimation error accuracy and precision. Conversely, the LMMSE algorithm may deteriorate the quality of the DC measurements whenever the ratio falls below a certain threshold.

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.

The Cramer-Rao lower bound (CRLB) is derived for the channel estimation of direct sequence-code division multiple access ultra-wideband (DS-CDMA UWB) communication system. We have modified the channel model and reduced the number of estimation parameters. Furthermore, a low-complexity data-aided Least Square (LS) channel estimation algorithm is proposed for the modified model. In addition, the problem of designing orthogonal training sequence is discussed. Finally, numerical examples are provided to evaluate both the derived CRLB and the proposed channel estimation algorithm.

In this paper, we propose a new use of LMMSE and Lr-LMMSE algorithms for channel estimation in OFDM (Orthogonal Frequency Division Multiplexing) system which, as known suffers from the time variation of the channel under high mobility conditions using block pilot insertion. The loss of sub channel orthogonality leads to inter-carrier interference (ICI). Using many algorithms for channel estimation, we will show that, for a 16- QAM modulation, the LMMSE algorithm performs well to achieve this estimation but when the SNR (Signal Noise Rate) is high, the three algorithms (LS, LMMSE and Lr-LMMSE) performs similarly. The challenge in this paper is that we will demonstrate that the LMMSE algorithm performs well with the block pilot insertion and is not suitable just with comb pilot insertion.

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.

The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave) frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted at both the transmitter and receiver to provide large array gains via directional beamforming. To achieve such array gains, channel estimation (CE) with high resolution and low latency is of great importance for mmWave communications. However, classic super-resolution subspace CE methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariant technique (ESPRIT) cannot be applied here due to RF chain constraints. In this paper, an enhanced CE algorithm is developed for the off-grid problem when quantizing the angles of mmWave channel in the spatial domain where off-grid problem refers to the scenario that angles do not lie on the quantization grids with high probability, and it results in power leakage and severe reduction of the CE performance. A new model is first proposed to formulate the off-grid problem. The new model divides the continuously-distributed angle into a quantized discrete grid part, referred to as the integral grid angle, and an offset part, termed fractional off-grid angle. Accordingly, an iterative off-grid turbo CE (IOTCE) algorithm is proposed to renew and upgrade the CE between the integral grid part and the fractional off-grid part under the Turbo principle. By fully exploiting the sparse structure of mmWave channels, the integral grid part is estimated by a soft-decoding based compressed sensing (CS) method called improved turbo compressed channel sensing (ITCCS). It iteratively updates the soft information between the linear minimum mean square error (LMMSE) estimator and the sparsity combiner. Monte Carlo simulations are presented to evaluate the performance of the proposed method, and the results show that it enhances the angle detection resolution greatly.

In this paper, a novel DFT-based pilot-symbol-aided channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems is presented. By using transform domain cluster and discriminant analysis, this algorithm can adaptively remove the noise in the channel impulse response (CIR). Then as the noise is removed, the accuracy of channel estimation can be improved dramatically. With a number of simulation experiments, the efficacy of the proposed channel estimation algorithm is validated by the numerical results.

ABSTRACTWhile inter-symbol interference is not of concern, inter-carrier interference that is caused by carrier frequency offset (CFO) is more concerned in orthogonal frequency division multiplexing (OFDM) systems. The estimation of channel impulse response (CIR) is another important parameter of interest in the system. Recently, a number of approaches for the joint estimation of CFO and CIR in OFDM systems have been proposed in the literature, and most of them are traditional maximum-likelihood-based approaches. In this paper, we propose a number of mixture cost-reference particle filtering (CRPF) approaches for joint estimation of CIR and CFO, and a couple of them can be applied without the knowledge of both state and measurement noise statistics. Conventional least mean squares algorithm or recursive least squares algorithm is jointly employed with recently developed CRPF for nonlinear and multiple parameters estimation in OFDM systems. The computer simulation results show that mixture CRPFs are more robust against particle impoverishment (PI) problem than standard mixture particle filtering (PF) and mixture regularized PF (RPF) approaches although RPF was particularly developed to tackle the PI problem. In this paper, we investigate a number of mixture PF approaches including ones that do not need the information of noise statistics for the joint estimation of CFO and channel in OFDM systems.