Academic literature on the topic 'Dynamic subcarrier mapping'

To reduce the detection failure of the exchanging signal power onto the OFDM subcarrier signal at uniform quantization, dynamic subcarrier mapping is applied. Moreover, to addressing low SNR’s wall less than pre-determine threshold, non-uniform quantization or adaptive quantization for the signal quantization size parameter is proposed. μ-law is adopted for adaptive quantization subcarrier mapping which is deployed in mobility environment, such as Doppler Effect and Rayleigh Fading propagation. In this works, sensing node received signal power then sampled into a different polarity positive and negative in μ-law quantization and divided into several segmentation levels. Each segmentation levels are divided into several sub-segment has representing one tone signal subcarrier number OFDM which has the number of quantization level and the width power. The results show that by using both methods, a significant difference is obtained around 8 dB compared to those not using the adaptive method.

To reduce the detection failure of the exchanging signal power onto the OFDM subcarrier signal at uniform quantization, dynamic subcarrier mapping is applied. Moreover, to addressing low SNR’s wall-less than pre-determine threshold, non-uniform quantization or adaptive quantization for the signal quantization size parameter is proposed. μ-law is adopted for adaptive quantization subcarrier mapping which is deployed in mobility environment, such as Doppler Effect and Rayleigh Fading propagation. In this works, sensing node received signal power then sampled into a different polarity positive and negative in μ-law quantization and divided into several segmentation levels. Each segmentation levels are divided into several sub-segment has representing one tone signal subcarrier number OFDM which has the number of quantization level and the width power. The results show that by using both methods, a significant difference is obtained around 8 dB compared to those not using the adaptive method.

В данной статье представлены результаты моделирования псевдослучайных чисел на основе системы Лоренца в методе селективного отображения снижения пик-фактора сигналов с ортогональным частотным мультиплексированием (OFDM). Проведено исследование влияния псевдослучайных чисел, формируемых на основе цифровой реализации системы Лоренца с динамическим хаосом, на эффективность снижения пик-фактора сигналов с ортогональным частотным мультиплексированием в методе селективного отображения и выполнен сравнительный анализ снижения пик-фактора сигналов метода селективного отображения (SLM) с использованием псевдослучайных чисел пакета Matlab и с использованием псевдослучайных чисел на основе системы Лоренца в методе селективного отображения снижения пик-фактора сигналов с ортогональным частотным мультиплексированием, количества сигнальных созвездий в квадратурной амплитудной модуляции, количества операций выполнения селективного отображения и изменения параметра r в системе Лоренца. Результаты данной работы могут применяться для снижения пик-фактора систем с ортогональным частотным мультиплексированием. Introduction. Currently, the reduction of peak-factor in signals employing orthogonal frequency division multiplexing is increasingly topical, given the active development and widespread use of this technology in various modern wireless wideband communication systems. This work aims to study the impact of pseudorandom numbers generated based on the digital implementation of the Lorentz system with dynamic chaos on the efficacy of reducing the peak-factor of signals utilizing orthogonal frequency multiplexing in the selective mapping method. Simulation results. The study evaluates the influence of pseudorandom numbers generated from the digital implementation of the Lorentz system with dynamic chaos on the effectiveness of reducing the peak-factor in signals with orthogonal frequency multiplexing using the selective mapping method. It includes a comparative analysis between the conventional selective mapping method and the use of pseudorandom numbers based on the Lorentz system in reducing the peak-factor of signals employing orthogonal frequency multiplexing. The peak-factor is assessed for different numbers of signal constellations in quadrature amplitude modulation, the number of operations for performing selective mapping, and variations in the parameter 'r' in the Lorentz system. Conclusions. This work investigated the influence of pseudorandom numbers generated from the digital implementation of the Lorentz system with dynamic chaos on the efficacy of reducing the peak-factor of signals employing orthogonal frequency multiplexing in the selective mapping method. The comparative analysis provided insights into the reduction of the peak-factor in signals with orthogonal frequency multiplexing using the selective mapping method. Additionally, the study analyzed the signal transmission coefficient with subcarrier modulation, variations in the number of subcarriers, and the impact of the Lorentz system's operating mode on reducing the peak-factor of signals employing orthogonal frequency division multiplexing.

The subject of the research is the processes of formation and processing of signals with orthogonal frequency divisionand multiplexing (OFDM) of chaotic sequences to ensure the stealthiness of data transmission. The research synthesizes the method for increasing the stealthiness of information transmission systems based on signals with OFDM-modulation on the basis of forming an analytical signal and chaotic mapping of Chebyshev polynomial. It would enable ensuring reliable information protection in radio transmission systems that use signals with OFDM-modulation, at the cost of the high level of structural and independent and Identically distributed (IID) (the degree of signal masking under noise) stealthiness of the signals. The tasks are to investigate the effectiveness of the developed method for increasing the stealthiness of information transmission systems by numerical assessment of the level of structural and IID-stealthiness and the quality of recovery of the masked information on the receiving side. The methods used are for the formation and processing of chaotic subcarriers in the signal with OFDM-modulation – methods of nonlinear dynamics, approaches to the formation of analytical chaotic signal and methods of the statistical theory of observation processing; to assess the level of structural and IID-stealthiness – steganography theory, a method of nonlinear time series analysis based on the use of BDS-statistics. The following results are obtained: the method for increasing the stealthiness of information transmission systems based on the use of signals with OFDM-modulation and chaotic subcarriers has been synthesized, has also evaluated the level of structural and IID-stealthiness of signals, that generated using the proposed method. It is established that compared with systems that use harmonic signals with OFDM-modulation, chaotic signals with OFDM-modulation can provide a higher level of IID-stealthiness. It was confirmed by the obtained results of visual, frequency, statistical and dynamic analysis. To assess the level of structural stealthiness, the expenditure of detecting the generated signals with a given probability has been estimated. The obtained results showed that the level of structural stealthiness increased by 2…2.5 times. It has shown that to ensure the required level of recovery of the generated signal, the signal-to-noise ratio at the input of the receiver must be greater than 4 dB. Conclusions. The scientific novelty of the obtained results lies in the following: for the first time, the method of subcarrier formation for signals with OFDM-modulation based on the use of analytical signal and Chebyshev polynomial of the first kind of tertiary is obtained. The proposed method provides the required level of structural and IID-stealthiness of information transmission systems, compared with conventional methods of signal generation with OFDM-modulation, due to the similarity of the generated signals with “white” noise.

To achieve better performance using multi carrier modulation we should make the subcarriers to be orthogonal to each other i.e. known as the Orthogonal Frequency Division Multiplexing (OFDM) technique. But the great drawback of the OFDM method is its high Peak to Average Power Ratio (PAPR). As we are using the linear power amplifier at the transmitter side so it’s operating point will go to the saturation region due to the high PAPR which leads to in-band distortion & out-band radiation. This can be evaded with growing the dynamic range of power amplifier which hints to high cost & high consumption of power at the base station.It is known that the PAPR reduction arrangements can be generallycategorized into two groups, i.e. distortion-less systems such as SLM & PTS & the pre-distortion systems such as clipping & companding. In this Paper, we demonstrate the performance of existing algorithms (PTS, SLM and Clipping) which has been compared using MALAB simulations. The proposed procedureexploitedstandard PTS, Clipping and SLM methods.We proved that by comparison of these PAPR reduction techniques, PTS perform much better than other standard methods like Amplitude clipping, selective mapping projected earlier with respect to CCDF.

In this article, we explore the use of neural networks (NNs) to enhance the performance evaluation of Universal Filtered Multi-Carrier (UFMC) systems, a key technology for modern communication networks such as 5G and beyond. Traditional methods of evaluating the Bit Error Rate (BER) and system efficiency in UFMC system can be computationally intensive and less accurate under dynamic conditions. To address these challenges, we propose a NN based approach that not only improves the accuracy of BER prediction but also significantly optimizes the system's overall efficiency. Channel state estimation (CSE) plays a major role for UFMC system to address the phenomenon of multipath channel fading. In order to achieve a high data rate using UFMC technology, it is necessary to have an effective CSE and very accurate signal detection. Recently, there has been significant interest in utilizing deep learning (DL) to enhance channel estimations. This article introduces a new method for channel estimation (CE) in UFMC system. The suggested approach utilizes DL models to improve the CE. For the UFMC system, we propose a detector based on bidirectional long short-term memory (Bi-LSTM). To identify the transmitted symbols, the suggested detector uses DL training data directly. Currently, a significant drawback of UFMC systems is the presence of a high peak-to-average power ratio (PAPR). The approach aims to reduce the BER and enhances the efficiency of the UFMC system. This is achieved by dynamically setting the constellation mapping and symbol damping on each subcarrier and sub-symbol. The results illustrate that the proposed model can accurately and efficiently recognize UFMC signals. The suggested model is being compared to Least Square (LS), LSTM, and Minimum Mean Square Error (MMSE) channel estimators. Through extensive simulations, our results demonstrate that the NN model reduces BER and enhances efficiency. The proposed model gives more effective performance in terms of enhanced efficiency and reduction of BER. The findings offer valuable insights for the design and optimization of next-generation communication systems, where accurate and efficient performance evaluation is critical.