Reduction of pilot overhead for channel estimation in OFDM systems using the regularized

Pilot assisted channel tracking (PACT) has been very popular for channel estimation in OFDM systems. However, as the mobility in the system increases and one has to maintain a high accuracy in channel estimation, the pilot overhead typically has to be increased. Emerging cellular OFDM standards are expected to use about 6 - 12% pilot overhead per stream, and any possible reduction in pilot overhead would be useful. Decision directed channel tracking (DDCT) can help reduce pilot overhead, but is known to suffer from error propagation at high fade rates. In emerging broadband wireless systems promising peak bit rates of 50 Mbps or more, saving on pilot overhead by using DDCT schemes would be highly attractive provided: (a) Such a DDCT approach does not suffer from error propagation and has an error rate performance comparable to (or better than) PACT schemes even at high fade rates; (b) The computational complexity of such a DDCT approach is not significantly more than that of the MMSE based PACT scheme. In this work, we propose a low complexity DDCT method which exploits the structure of the regression matrix in conjunction with robust statistics to mitigate the effect of error propagation. It has a much lower computational complexity and a better error rate performance than the decision directed EM-Kalman and other existing robust statistics based DDCT schemes. The proposed method also outperforms a 12.5% pilot overhead based PACT scheme with only a modest increase in computational complexity.

Channel estimation can be classified into pilot‐assisted channel estimation, blind channel estimation, and decision DDCE. This chapter focuses on pilot‐assisted channel estimation in OFDM systems. There are two main design issues for channel estimation in OFDM systems. The first one is pilot design and the second one is channel estimator design with both low complexity and accuracy. An interpolation technique should be performed to obtain a channel estimation value at each data subcarrier. Channel estimation for MIMO‐OFDM systems is more complicated than single antenna systems but the basic principle is similar to the single antenna systems. The purpose of equalizers is to find the inverse function of the channel response and eliminate an Inter‐Symbol Interference (ISI) caused by the dispersive nature of the channel. The channel estimator and the equalizer are designed according to the system requirements such as pilot assignment, antenna array, system complexity, latency, and power.

The development of internet related applications and their increasing demands have led to advancement in high speed communication systems. For the accomplishment of these increasing demands MIMO channels with multiple transmitting and receiving antennas have been devised. MIMO systems enhance the capacity using spatial multiplexing and interference reduction through spatial dimensioning. Furthermore, MIMO systems mutual with orthogonal frequency division multiplexing (MIMO-OFDM) improves system capacity and mitigation against fading. These systems are the basis of 4G and beyond wireless communication standards. This research work focus on improvement in channel estimation performance using various techniques on standard communication models. Semi blind channel estimation technique is employed for channel estimation, which includes use of training sequence in form of pilots. Block type arrangement is being used, since it’s based on frequency selective channels. LSE and MMSE estimations are the two basic estimation techniques which can be efficiently used to estimate channels in OFDM systems. For MMSE implementation information of noise variance and channel covariance is required. Furthermore it is extremely complex than LSE estimator. DFT and DCT based channel estimation methods are employed to increase the performance and diminish the complexity. Different interpolation methods are been used and analyzed for better performance. Number of subcarriers which also have an impact on estimation performance has also been discussed. Overall channel estimation performance have been examined for both large and small scale fading channel models.

In this article, design of preamble for channel estimation and pilot symbols for pilot-assisted channel estimation in orthogonal frequency division multiplexing system with null subcarriers is studied. Both the preambles and pilot symbols are designed to minimize the l2 or the l∞ norm of the channel estimate mean-squared errors (MSE) in frequency-selective environments. We use convex optimization technique to find optimal power distribution to the preamble by casting the MSE minimization problem into a semidefinite programming problem. Then, using the designed optimal preamble as an initial value, we iteratively select the placement and optimally distribute power to the selected pilot symbols. Design examples consistent with IEEE 802.11a as well as IEEE 802.16e are provided to illustrate the superior performance of our proposed method over the equi-spaced equi-powered pilot symbols and the partially equi-spaced pilot symbols.

This paper presents SLRTNet, a deep learning model designed for channel estimation in OFDM systems. SLRTNet employs a super-resolution network in combination with a transformer architecture, which features an improved encoder-decoder structure. To extract essential input information, a transformer encoder with a multi-head attention mechanism and a Feedforward Net that utilizes LRR Blocks are used. A multi-level residual network is implemented as a transformer decoder to improve the model's generalization ability. The simulation results show that SLRTNet outperforms other deep learning-based neural network schemes. Compared with the conventional LS algorithm, SLRTNet exhibits a performance gain of 0.7 to 2 dB in the low signal-to-noise ratio (SNR) regime and 5.5 to 8 dB in the high SNR regime. This indicates that SLRTNet has good performance in channel estimation for OFDM systems and improves the accuracy of channel estimation effectively.

Channel estimation is critical for coherent detection in OFDM systems. In pilot symbol aided channel estimation methods, the LS channel estimation can be viewed as a noisy observation of the true channel frequency response, so the noise component can be compressed through signal subspace projecting. In this paper, we focus on parametric channel estimation method combined with subspace projecting. First, we estimate the signal space of the OFDM system, and then we can use ESPRIT method to estimate the multipath delay of wireless channel, finally we use subspace projecting to get more accurate channel estimation. When the channel multipath delays vary with time, the signal subspace should be tracked to keep the efficiency of channel estimation. Computer simulations show that the performance of our method is much better than traditional channel estimation methods and its nonparametric counterpart.

OFDM technique uses multiple sub-carriers for the data transmission. Therefore, bit error rate increases because of inter-carrier interference caused by nonlinear high power amplifier and carrier frequency offset. Wireless OFDM transmission over multi path fading channels is characterized by small transmission gain in multiple sub-carrier frequency interval. Therefore bit error rate increases because of burst errors. Inter-leaver and convolution error control coding are effective for the reduction of this burst error. Pilot symbol is used for the channel estimation in OFDM systems. However, imperfect channel estimates in this systems degrade the performance. The performance of this convolution coding OFDM systems using inter-leaver, gauged by the bit error rate, is analyzed considering the nonlinear high power amplifier, carrier frequency offset and channel estimation error.

In this paper, we propose a robust channel estimation method in OFDM systems over fast fading channels. In consideration of the effect of null subcarriers, the proposed channel estimation is first performed in the frequency domain. By making efficient use of channel correlation and the property of ICI to reduce the number of needed parameters for channel estimation, robust channel estimation for ICI is accomplished in parallel, pilot cluster by pilot cluster. It is then transformed into the time domain to obtain a gain in noise reduction and to reduce interpolation error as well. Simulation results show that the proposed channel estimation has good performance compared to previous works.

The minimum mean-square error (MMSE) criterion is often used for channel estimation in wireless OFDM systems. The MMSE channel estimators perform better than the least-square (LS) criterion-based techniques but their computational complexity is one of the main drawbacks for practical implementation, especially when the number of subcarriers is large. To address this issue while maintaining the performance advantage, we present a new simplified MMSE channel estimator for OFDM systems in multipath fading channels. The proposed method is primarily based on the rank-reduction of the channel correlation matrix in the frequency domain. Simulation results show that the proposed method achieves almost the same performance as the full-rank MMSE method, while significantly reducing the computational complexity.

In OFDM systems, channel estimation is a crucial consideration for improving system performance. The channel fading effects are eliminated by the equalization of received signal, and the equalizer coefficients are determined according to the results of channel estimation. In this paper, the channel estimation of OFDM systems in time domain for fast fading channel is investigated and developed. The time-domain channel estimation scheme is achieved by inserting pseudo-noise (PN) sequences in the prefix of each OFDM symbol, and therefore the channel impulse response can be estimated. These proposed algorithms include both least-squares and maximum-likelihood estimation techniques. The proposed techniques can perform better than the conventional channel estimation with correlators. Some comparative simulations are given to illustrate the superior performance of the proposed channel estimation techniques.

A novel data-aided channel estimation for OFDM systems based on selective superimposed pilot sequences is proposed in this paper, where the receiver correlates the received signal sequence with the pilot symbol sequence, and obtains the channel estimation. However, the pilot sequence is superimposed on a data sequence and transmitted together, and thus it has a high spectral efficiency. The selective pilot sequence owns low correlation with data sequence to reduce the interference. In the case of Pilot=1 and L=31, BER of the proposed channel estimation is improved about 2%. With the increase of L and pilot, BER gets lower. So by taking advantage of the correlation between pilot sequences themselves, the proposed algorithm can achieve the good channel estimation.

Compressive sensing (CS) techniques have been shown to enhance the performance of sparse channel estimation. This paper addresses CS-based sparse channel estimation in practical systems where the measurements are quantized to a finite number of bits. In these systems, quantization introduces two kinds of errors into the CS framework: quantization errors and saturation errors. To combat the effect of quantization error on estimating the sparse channel, a new complex-valued cost function is formulated based on the quantization model. To minimize this cost function, a multi-bit iterative hard thresholding algorithm is proposed. The channel estimate performance is evaluated by modeling the quantization error as white Gaussian noise. Our simulation results quantify the number of bits needed for our proposed quantized CS-based sparse channel estimate to achieve satisfactory performance.

The paper proposes novel carrier frequency offset estimation algorithm that exploits scattered pilot symbols used for channel estimation of OFDM systems. Carefully designed pilot symbols can be incorporated to form null subcarriers by trivial manipulation of the received OFDM signals so that null subcarrier based maximum likelihood CFO estimation method can be applied, whereas conventional maximum likelihood CFO estimation using pilot symbols jointly estimates CFO and channel impulse response iteratively which is impractical. Simulation results show satisfactory performance for frequency tracking stage. The paper also provides reduced order polynomial rooting method for simple and fast CFO estimation.

We investigate a practical pilot design for indoor channels with an analysis of channel estimation for block and comb-type pilot arrangements. For the pilot design over indoor channels, the performance is determined by the tradeoff between the interpolation error by frequency selectivity of the channel and the noise reduction by the interpolator, compared to the Doppler effect. Results show that the performance of channel estimation for the comb-type pilot symbol, with respect to the derived criterion, outperforms that of the block-type pilot symbol for indoor channels, whereas it is a slow-fading environment.

Multiple transmit and receive antennas (MIMO) have been used in OFDM systems for capacity improvement. In practice, channel state information has to be estimated for diversity combining or space-time decoding. Previous work on channel estimation assumes that MIMO channels are independent and identically distributed (i.i.d.). In certain propagation environments, there exists spatial correlation among channels corresponding to different pairs of transmit and receive antennas. The spatial correlations can he exploited to improve channel estimation. In this paper, we develop a minimum mean-square-error (MMSE) channel estimator for MIMO-OFDM systems that can make full use of the spatial correlation. We also design optimum training sequences that minimize the channel estimation error. When MIMO channels are i.i.d., the training sequences for different transmit antennas are orthogonal and with equal power. However, when MIMO channels are spatially correlated, the power allocation for training sequences can be further optimized. Our simulation results show that the proposed MMSE estimator can exploit spatial and frequency correlations of MIMO channels in OFDM systems and therefore has good performance.