Power allocation of data dependent superimposed training

Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing theory and prior available knowledge of channel's sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2, 3.5, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for $$\mathrm {BER}=10^{-1.9}$$BER=10-1.9, the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1, 2.5, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity.

In single carrier (SC) systems using a data-dependent superimposed training (DDST) scheme, the data-induced interference that occurs during channel estimation is eliminated, but at the expense of data distortion. This distortion causes a data identification problem (DIP), in which the data sequence cannot be uniquely identified at the receiver end. Accordingly, the present study proposes a precoding scheme which solves the DIP in SC systems using DDST. Five design criteria are derived for the precoding matrix in order to eliminate DIP, achieve frequency diversity, maintain a low peak-to-average power ratio (PAPR), and minimize the receiver complexity. In addition, a low-complexity detection method is proposed for reducing the search space of the detection process. The simulation results show that the proposed precoding scheme successfully eliminates the DIP in traditional DDST schemes while maintaining the same PAPR as that of the traditional SC systems based on DDST.

This paper presents a novel iterative detection and channel estimation scheme that combines the effort of superimposed training (ST) and pilot-aided training (PAT) for multiple-input multiple-output (MIMO) flat fading channels. The proposed method, hereafter known as joint mean removal ST and PAT (MRST-PAT), implements an iterative detection and channel estimation that achieves the performance of data-dependent ST (DDST) algorithm, with the difference that the data arithmetic cyclic mean is estimated and removed from data at the receiver’s end. It is demonstrated that this iterative and cooperative detection and channel estimator algorithm surpasses the effects of data detection identifiability condition that DDST has shown when higher orders of modulation are used. Theoretical performance of the MRST-PAT scheme is provided and corroborated by numerical simulations. In addition, the performance comparison between the proposed method and different MIMO channel estimation techniques is analyzed. The joint effort between ST and PAT shows that MRST-PAT is a solid candidate in communications systems for multiamplitude constellations in Rayleigh fading channels, while achieving high-throughput data rates with manageable complexity and bit-error rate (BER) as a figure of merit.

Block synchronization is a prerequisite to data-dependent superimposed training (DDST) based channel estimation. Mainly designed for single input single output (SISO) system, the existing block synchronization algorithms can hardly work for multi-input multi-output (MIMO) system. A new joint block synchronization, channel and dc-offset estimation algorithm based on balanced zero correlation zone (ZCZ) sequence for MIMO system is proposed in this paper. Analysis and simulation results show that the new algorithm has the same performance for SISO systems as the existing algorithms when their block and cyclic prefix lengths are the same. While for MIMO systems, the performance of the proposed algorithm is much better than that of the existing algorithms.

A recent improvement in superimposed training for channel estimation, where the training sequence is actually added to the information data, is called data-dependent superimposed training (DDST). Here we show (theoretically and via simulations) that the performance of DDST (both for channel estimation and equalisation) may suffer minimal degradation when implemented without the use of a cyclic prefix (which carries only redundant information).

Channel estimation for frequency-selective time- varying channels is considered using superimposed training. We employ a discrete prolate spheroidal basis expansion model (DPS-BEM) to describe the time-varying channel. A periodic (non-random) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission. In existing first-order statistics-based channel estimators, the information sequence acts as interference resulting in a poor signal-to- noise ratio (SNR). In this paper a data-dependent superimposed training sequence is used to either totally or partially cancel out the effects of the unknown information sequence at the receiver on channel estimation. In total cancellation, at certain frequencies, the information-bearing components are nulled. To compensate for this information loss, we propose a partially-data- dependent (PDD) superimposed training scheme where a tradeoff is made between interference cancellation and frequency integrity. An iterative method is also used to enhance channel estimation and data detection and illustrated via a simulation example.

Achieving accurate channel estimation and adaptive communications with moving transceivers is challenging due to rapid changes in the underwater acoustic channels. We achieve an accurate channel estimation of fast time-varying underwater acoustic channels by using the superimposed training scheme with a powerful channel estimation algorithm and turbo equalization, where the training sequence and the symbol sequence are linearly superimposed. To realize this, we develop a ‘global’ channel estimation algorithm based on Gaussian likelihood, where the channel correlation between (among) the segments is fully exploited by using the product of the Gaussian probability-density functions of the segments, thereby realizing an ideal channel estimation of each segment. Moreover, the Gaussian-likelihood-based channel estimation is embedded in turbo equalization, where the information exchange between the equalizer and the decoder is carried out in an iterative manner to achieve an accurate channel estimation of each segment. In addition, an adaptive communication algorithm based on constellation aggregation is proposed to resist the severe fast time-varying multipath interference and environmental noise, where the encoding rate is automatically determined for reliable underwater acoustic communications according to the constellation aggregation degree of equalization results. Field experiments with moving transceivers (the communication distance was approximately 5.5 km) were carried out in the Yellow Sea in 2021, and the experimental results verify the effectiveness of the two proposed algorithms.

The acquisition of accurate channel estimation is important for Orthogonal Frequency-Division Multiplexing (OFDM) system to reliably recover the transmitted data symbols. Decision directed channel estimation (DDCE) has been utilized to improve the accuracy of conventional training-based channel estimation. However, this method can only provide limited improvement when the data decision feedback comprises a large portion of decision errors at low signal-to-noise (SNR) range. A new Iterative DDCE (IDDCE) scheme with reliable data feedback selection is proposed in this paper. The unreliable data decisions are eliminated on the subcarriers where noise components are enhanced due to the impact of frequency selective channel. An explicit expression of the variance of channel estimation error is derived, based on which the optimal selection criteria are also derived to minimize the variance. Simulation results show that the accuracy of channel estimation is significantly improved with the proposed algorithm.

In this paper, we evaluate the computational complexity and the performance of the RAKE receivers for the Direct Sequence---Ultra Wideband (DS-UWB) with considering the accuracy of channel estimation in a multipath channel. As RAKE receivers for DS-UWB, we consider the maximal-ratio combining (MRC)-RAKE, the minimum mean square error (MMSE)-RAKE, and the MRC-RAKE-Equalizer that is the MRC-RAKE followed by a liner equalizer. Generally, if the channel estimation is perfect, as the number of fingers or taps increases, the performance of each receiver is improved, however the computational complexity of each receiver increases. In practice, the channel estimation is not perfect. The channel estimation error makes their performances degraded. Therefore, the performances of the RAKE receivers depend on the accuracy of channel estimation. Consequently, we evaluate the computational complexities and the Bit Error Rates (BERs) of MRC-RAKE, MMSE-RAKE, and MRC-RAKE-Equalizer with considering the accuracy of channel estimation for DS-UWB. We show that the accuracy of channel estimation affects the BER of each receiver significantly. We also show that when the accuracy of channel estimation is high, MRC-RAKE-Equalizer can achieve the better BER than MMSE-RAKE with less computational complexity, while MMSE-RAKE can achieve the better BER than MRC-RAKE-Equalizer when the accuracy of channel estimation is low.

The limited fronthaul capacity imposes a challenge on the uplink of a centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of a massive multiple-input multiple-output (MIMO)-aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise ratio for the massive MIMO-aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming at maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by the RRU and 128 RAs deployed by the BBU, the sum rate of 10 UDs achieved with the optimal power sharing factors improves by 33% compared with the one attained without optimizing power sharing factors.

Orthogonal frequency division multiplexing (OFDM) technology has been increasingly applied to power line carrier communication (PLC). Discrete Fourier Transformation (DFT)‐based channel estimation algorithm is suitable for OFDM‐based PLC due to its low complexity. In view of the problem that the traditional DFT channel estimation does not consider the influence of noise inside cyclic prefix (CP), an improved DFT channel estimation based on signal‐to‐noise ratio (SNR) estimation is proposed. First, least‐square (LS) algorithm is performed and the frequency domain channel estimation is converted to the time domain through inverse DFT, and the average SNR of the system is estimated according to the pilot sequence. Second, the substitute SNR for each sample point inside CP is defined and used to filter impulse noise inside CP. Third, the average SNR is converted into the threshold of the useful signal energy inside CP, and the widespread background noise inside CP is filtered. The simulation results show that the proposed algorithm can obtain more accurate channel estimation than other DFT channel estimation algorithms because it can effectively filter out noise inside CP. In addition, compared with other similar algorithms, the proposed algorithm dose not result in a significant increase in complexity.

In this paper, we address the problems of super-imposed training (ST)-aided channel estimation for OFDM systems over rapidly time-varying (RTV) multi-path channels. Based on the piece-wise linear assumption, the superimposed training sequences are optimized with regard to minimizing the channel mean square error. Meanwhile, we derive a closed-form expression of channel estimation analysis. Simulation results show that the proposed design of training sequence outperforms that of the existing ST for RTV channel estimation.

To improve the bandwidth efficiency, a semi-blind channel estimation method using data nulling superimposed pilots (DNSP) is designed for orthogonal frequency division multiplexing (OFDM) system without data rate loss. However, the data distortion is introduced by rank-deficient precoding. To minimize the data distortion, the optimal precoder is found to minimize the Euclidean distance between precoded sequence and source sequence. Further, the impact of power allocation between training sequence and data information on effective signal noise ratio (SNR) is analytically derived, effective SNR is found to be a quadratic function for the percentage allocated for training sequence, and the optimal power allocation is provided to maximize the effective SNR. Simulation results show that our optimal precoder design and power allocation scheme obtain the optimal performance of symbol detection.

We consider the problem of data-aided frequency-offset and channel estimation in the case of frequency-selective channels. More precisely, we address the problem of training sequence selection with the goal of providing accurate frequency offset and channel estimates. Toward this end, we consider the Crame/spl acute/r-Rao bound (CRB), for which we derive a closed-form expression. Since the CRB is a complicated function of the training sequence and the channel parameters, a much simpler asymptotic CRB is derived. Two criteria for training sequence design based on the asymptotic CRB are proposed, and a minmax approach is presented to optimize them. Our main contribution is to show that a white sequence is minmax optimal for both criteria considered, and that the quest for a generally optimal sequence is hardly motivated.

In this paper, we present a tensor MIMO-GFDM system model that is based on the double contraction operator. Based on the derived system model, we propose an iterative tensor based MIMOGFDM receiver, that is initialized with the channel estimation obtained via pilots transmitted in the first data frame. The proposed algorithm exploits the tensor structure by using several unfoldings of the received signal sequentially to obtain estimates of the transmitted symbols and the channel. Simulation results show the tensor gain for the proposed algorithm in addition to the improved channel estimation. Numerical results confirm that the receiver requires the same amount of pilots as the Zero Forcing (ZF) receiver, while having a better symbol error rate (SER) performance and a better channel estimation accuracy.