1,007 research outputs found
MIMO-UFMC Transceiver Schemes for Millimeter Wave Wireless Communications
The UFMC modulation is among the most considered solutions for the
realization of beyond-OFDM air interfaces for future wireless networks. This
paper focuses on the design and analysis of an UFMC transceiver equipped with
multiple antennas and operating at millimeter wave carrier frequencies. The
paper provides the full mathematical model of a MIMO-UFMC transceiver, taking
into account the presence of hybrid analog/digital beamformers at both ends of
the communication links. Then, several detection structures are proposed, both
for the case of single-packet isolated transmission, and for the case of
multiple-packet continuous transmission. In the latter situation, the paper
also considers the case in which no guard time among adjacent packets is
inserted, trading off an increased level of interference with higher values of
spectral efficiency. At the analysis stage, the several considered detection
structures and transmission schemes are compared in terms of bit-error-rate,
root-mean-square-error, and system throughput. The numerical results show that
the proposed transceiver algorithms are effective and that the linear MMSE data
detector is capable of well managing the increased interference brought by the
removal of guard times among consecutive packets, thus yielding throughput
gains of about 10 - 13 . The effect of phase noise at the receiver is also
numerically assessed, and it is shown that the recursive implementation of the
linear MMSE exhibits some degree of robustness against this disturbance
A Survey of Blind Modulation Classification Techniques for OFDM Signals
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed
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