Partial-Data Superimposed Training with Data Precoding for OFDM Systems

Superimposed training (ST) is a recently addressed technique used for channel estimation where known training sequences are arithmetically added to data symbols, avoiding the use of dedicated pilot subcarriers, and thus, increasing the available bandwidth compared with traditional pilot symbol assisted modulation schemes. However, the system handles data interference over channel estimation as a result of the ST process; also, data detection is degraded by pilot interference. Recent ST methods have analyzed the data interference and presented schemes that deal with it. We propose a novel superimposed model over a precoded data scheme, named partial-data superimposed training (PDST), where an interference control factor assigns the adequate information level to be added to the training sequence in orthogonal frequency division multiplexing systems. Also, a data detection method is introduced to improve the symbol error rate performance. Moreover, a capacity analysis of the system has been derived. Finally, simulation results confirm that performance of PDST is superior to previous proposals

Convolutive superposition for multicarrier cognitive radio systems

Recently, we proposed a spectrum-sharing paradigm for single-carrier cognitive radio (CR) networks, where a secondary user (SU) is able to maintain or even improve the performance of a primary user (PU) transmission, while also obtaining a low-data rate channel for its own communication. According to such a scheme, a simple multiplication is used to superimpose one SU symbol on a block of multiple PU symbols.The scope of this paper is to extend such a paradigm to a multicarrier CR network, where the PU employs an orthogonal frequency-division multiplexing (OFDM) modulation scheme. To improve its achievable data rate, besides transmitting over the subcarriers unused by the PU, the SU is also allowed to transmit multiple block-precoded symbols in parallel over the OFDM subcarriers used by the primary system. Specifically, the SU convolves its block-precoded symbols with the received PU data in the time-domain, which gives rise to the term convolutive superposition. An information-theoretic analysis of the proposed scheme is developed, which considers different amounts of network state information at the secondary transmitter, as well as different precoding strategies for the SU. Extensive simulations illustrate the merits of our analysis and designs, in comparison with conventional CR schemes, by considering as performance indicators the ergodic capacity of the considered systems.Comment: 29 pages, 8 figure

A Novel PTS Scheme for PAPR Reduction of Filtered-OFDM Signals without Side Information

In this paper, a novel partial transmit sequence (PTS) scheme is proposed for reducing the peak-to-average power ratio (PAPR) of filtered orthogonal frequency division multiplexing (f-OFDM) systems. The PTS method is modified such that no side information (SI) transmission is needed. The data and pilot recovery are accomplished by a simple detector, making use of the correlation property of the Hadamard sequence and the transparency property of the pilot signal and an iterative phase detection is further added in a fading channel. Simulation results show that the modified solution provides a higher correct detection probability without increasing the system complexity nor affecting the PAPR suppression performance

Channel estimation techniques for next generation mobile communication systems

Mención Internacional en el título de doctorWe are witnessing a revolution in wireless technology, where the society is demanding new services, such as smart cities, autonomous vehicles, augmented reality, etc. These challenging services not only are demanding an enormous increase of data rates in the range of 1000 times higher, but also they are real-time applications with an important delay constraint. Furthermore, an unprecedented number of different machine-type devices will be also connected to the network, known as Internet of Things (IoT), where they will be transmitting real-time measurements from different sensors. In this context, the Third Generation Partnership Project (3GPP) has already developed the new Fifth Generation (5G) of mobile communication systems, which should be capable of satisfying all the requirements. Hence, 5G will provide three key aspects, such as: enhanced mobile broad-band (eMBB) services, massive machine type communications (mMTC) and ultra reliable low latency communications (URLLC). In order to accomplish all the mentioned requirements, it is important to develop new key radio technologies capable of exploiting the wireless environment with a higher efficiency. Orthogonal frequency division multiplexing (OFDM) is the most widely used waveform by the industry, however, it also exhibits high side lobes reducing considerably the spectral efficiency. Therefore, filter-bank multi-carrier combined with offset quadrature amplitude modulation (FBMC-OQAM) is a waveform candidate to replace OFDM due to the fact that it provides extremely low out-ofband emissions (OBE). The traditional spectrum frequencies range is close to saturation, thus, there is a need to exploit higher bands, such as millimeter waves (mm-Wave), making possible the deployment of ultra broad-band services. However, the high path loss in these bands increases the blockage probability of the radio-link, forcing us to use massive multiple-input multiple-output (MIMO) systems in order to increase either the diversity or capacity of the overall link. All these emergent radio technologies can make 5G a reality. However, all their benefits can be only exploited under the knowledge and availability of the channel state information (CSI) in order to compensate the effects produced by the channel. The channel estimation process is a well known procedure in the area of signal processing for communications, where it is a challenging task due to the fact that we have to obtain a good estimator, maintaining at the same time the efficiency and reduced complexity of the system and obtaining the results as fast as possible. In FBMC-OQAM, there are several proposed channel estimation techniques, however, all of them required a high number of operations in order to deal with the self-interference produced by the prototype filter, hence, increasing the complexity. The existing channel estimation and equalization techniques for massive MIMO are in general too complex due to the large number of antennas, where we must estimate the channel response of each antenna of the array and perform some prohibitive matrix inversions to obtain the equalizers. Besides, for the particular case of mm-Wave, the existing techniques either do not adapt well to the dynamic ranges of signal-to-noise ratio (SNR) scenarios or they assume some approximations which reduce the quality of the estimator. In this thesis, we focus on the channel estimation for different emerging techniques that are capable of obtaining a better performance with a lower number of operations, suitable for low complexity devices and for URLLC. Firstly, we proposed new pilot sequences for FBMC-OQAM enabling the use of a simple averaging process in order to obtain the CSI. We show that our technique outperforms the existing ones in terms of complexity and performance. Secondly, we propose an alternative low-complexity way of computing the precoding/postcoding equalizer under the scenario of massive MIMO, keeping the quality of the estimator. Finally, we propose a new channel estimation technique for massive MIMO for mm-Wave, capable of adapting to very variable scenarios in terms of SNR and outperforming the existing techniques. We provide some analysis of the mean squared error (MSE) and complexity of each proposed technique. Furthermore, some numerical results are given in order to provide a better understanding of the problem and solutions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Antonia María Tulino.- Secretario: Máximo Morales Céspedes.- Vocal: Octavia A. Dobr

Effects of channel estimation on multiuser virtual MIMO-OFDMA relay-based networks

A practical multiuser cooperative transmission scheme denoted as Virtual Maximum Ratio Transmission (VMRT) for multiple-input multiple-output-orthogonal frequency division multiple access (MIMO-OFDMA) relay-based networks is proposed and evaluated in the presence of a realistic channel estimation algorithm and using low-density parity-check (LDPC) codes. It is shown that this scheme is robust against channel estimation errors. It offers diversity and array gain, keeping the complexity low with a multiuser and multiantenna channel estimation algorithm that is simple and efficient. In addition, the combination with LDPC codes provides improved gains; diversity gains larger than 6 dB can be easily obtained with a reduced number of relays. Thus, this scheme can be used to extend coverage or increase system throughput by using simple cooperative OFDMA-based relays.The authors would like to thank Jae-Yun Ko for his valuable help at the beginning of our work. This work has been partly funded by the projects MULTIADAPTIVE (TEC2008-06327- C03-02), COMONSENS (CSD2008-00010) and CODIV (ICT-2007-215477).Publicad

Parametric approximation to optimal averaging in superimposed training schemes under realistic time-variant channels

Proceedings of: 13th IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, 20-22 July 2022, Porto, Portugal.Superimposed Training (ST) with orthogonal frequency division multiplexing (OFDM) scheme has become an attractive solution to meet the goals of the fifth generation (5G) of mobile communications, by improving the channel estimation performance, which is one of the main challenge in multiple input multiple output (MIMO) systems. This technique does not hinder the throughput, however, it introduces an intrinsic interference since the data and the reference symbols are sent together. In order to mitigate it, several studies propose a time averaging over several OFDM received symbols, where the optimal length of this averaging can be analytically computed by solving a transcendental equation. In this paper, this optimal averaging is approximated by a low complexity parametric approach based on a multiple linear regression model that inputs two parameters, the signal-to-noise ratio (SNR) and the relative speed between the transmitter and receiver, which effectively represents the variability of the channel in time. Results show that the approximated solutions give an error of 0.05% on average and 7% at most in terms of the provided mean square error (MSE) of the channel estimation.This work has been supported by a fellowship from the Spanish Ministry of Science and Innovation, under grant PRE2018-084315, and, by the Spanish National Project IRENE-EARTH (PID2020-115323RB-C33/AEI/10.13039/501100011033)

Parametric model and estimator classifier for optimal averaging in mobile OFDM systems with superimposed training

Proceedings of: 2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), 25-28 June 2023, Jeju, Republic of Korea.Superimposed training (ST) is an attractive technique for channel estimation in orthogonal frequency division multiplexing (OFDM) modulation. However, its main challenge is the intrinsic interference due to the joint transmission of pilot and data symbols, which can be mitigated by averaging the received signal. Previous works analyzed the mean square error (MSE) of the channel estimation, for both least squares (LS) and minimum MSE (MMSE) estimators, and showed that, under realistic channel models, the optimum number of averaged symbols could be computed by solving a transcendental equation. In this paper, as a practical implementation proposal, these optimum averaging values are parametrically approximated with a multilinear regression model. Also, it is proposed an accurate classifier that, under delay and performance tolerances, is able to select the most suitable estimator between LS and MMSE