Channel estimation technique with assistance of PN-coded training sequences for wireless OFDM communications

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.

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.

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.

In practical OFDM systems, some virtual subcarriers are reserved for easing the requirements on the filter. In this case, the channel impulse response (CIR) estimated by DFT approaches appears a spectral leakage, which results in an error floor for the MSE performance. This paper proposes two enhanced DFT interpolation-based channel estimation methods, termed as EDFTI-CE-A and EDFTI-CE-B, based on the robust Wiener filtering, which can eliminate most of the leakage effect owning to the absence of pilot symbols in the virtual subcarriers and further suppress the effect of additive noise. The analysis and simulation results show that the proposed EDFTI-CE-A can almost eliminate the MSE floor of conventional DFTI-CE with small complexity increase and the proposed EDFTI-CE-B can achieve the same performance as linear MMSE channel estimator but with much lower computational complexity.

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.

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 paper, we investigate the tracking of time-varying channels in OFDM systems using both preamble and pilot symbols. A regularized least-squares method will be used to incorporate the initial channel estimate from the preamble to improve the performance of pilot-assisted channel estimation. Instead of using a fixed regularization parameter, the proposed method adaptively determines the regularization parameter according to mean-squared error of the latest channel estimate and that of the standard LS estimate. Simulation results demonstrate the proposed method can outperform the standard LS method with lesser pilots, thus achieving the reduction of pilot overhead especially at lower SNR environments.

Massive multiple-input multiple-output (massive-MIMO) is foreseen as a potential technology for future 5G cellular communication networks due to its substantial benefits in terms of increased spectral and energy efficiency. These advantages of massive-MIMO are a consequence of equipping the base station (BS) with quite a large number of antenna elements, thus resulting in an aggressive spatial multiplexing. In order to effectively reap the benefits of massive-MIMO, an adequate estimate of the channel impulse response (CIR) between each transmit–receive link is of utmost importance. It has been established in the literature that certain specific multipath propagation environments lead to a sparse structured CIR in spatial and/or delay domains. In this paper, implicit training and compressed sensing based CIR estimation techniques are proposed for the case of massive-MIMO sparse uplink channels. In the proposed superimposed training (SiT) based techniques, a periodic and low power training sequence is superimposed (arithmetically added) over the information sequence, thus avoiding any dedicated time/frequency slots for the training sequence. For the estimation of such massive-MIMO sparse uplink channels, two greedy pursuits based compressed sensing approaches are proposed, viz: SiT based stage-wise orthogonal matching pursuit (SiT-StOMP) and gradient pursuit (SiT-GP). In order to demonstrate the validity of proposed techniques, a performance comparison in terms of normalized mean square error (NCMSE) and bit error rate (BER) is performed with a notable SiT based least squares (SiT-LS) channel estimation technique. The effect of channels’ sparsity, training-to-information power ratio (TIR) and signal-to-noise ratio (SNR) on BER and NCMSE performance of proposed schemes is thoroughly studied. For a simulation scenario of: 4 × 64 massive-MIMO with a channel sparsity level of 80 % and signal-to-noise ratio (SNR) of 10 dB , a performance gain of 18 dB and 13 dB in terms of NCMSE over SiT-LS is observed for the proposed SiT-StOMP and SiT-GP techniques, respectively. Moreover, a performance gain of about 3 dB and 2.5 dB in SNR is achieved by the proposed SiT-StOMP and SiT-GP, respectively, for a BER of 10 − 2 , as compared to SiT-LS. This performance gain NCME and BER is observed to further increase with an increase in channels’ sparsity.

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.

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.

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.

Inherent inter-symbol and inter-carrier interference elimination ability of cyclic prefixed OFDM transmission fails for the case of multipath fading channels when the channel impulse response (CIR) length exceeds the duration of cyclic prefix (CP). Conventional channel estimation and equalization schemes, if applied to this case of insufficient CP, suffer significant performance degradation. We propose, in this paper, a channel estimation scheme that enables estimation of the complete CIR even beyond the CP length. We then design an optimal MMSE based equalizer for the suppression of insufficient CP generated interference. A robust and low complexity version of this equalizer is also derived. Simulation results for the proposed schemes show significant performance gain at low SNRs and drastic reduction of the error floors at high SNRs and more importantly, as opposed to earlier schemes, without any loss in transmission efficiency.

This paper presents the performance of two channel estimation (CE) techniques for MC-CDMA systems. One is time domain version, where multipath delay profile is estimated and transformed to propagation characteristics of each subcarrier. The other is frequency domain version, where the received signal at the output of fast Fourier transform (FFT) is correlated by the known pilot signal in each subcarrier. We analyze the performance of the two CE techniques and show the superiority of time domain version over frequency domain version. We also show that the performance of the time domain version is dependent on the type of pilot symbols used in MC-CDMA systems.

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 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.