scholarly journals Precoded Faster-than-Nyquist Signaling with Optimal Power Allocation in Frequency-Selective Channel

2021 ◽  
Author(s):  
Takumi Ishihara ◽  
Shinya Sugiura
Keyword(s):  
Mutual Information ◽  
Power Allocation ◽  
Shaping Filter ◽  
Optimal Power ◽  
Power Spectral ◽  
Bit Error Ratio ◽  
Selective Channel ◽  
Frequency Selective ◽  
Eigen Decomposition ◽  
Performance Results

<p>In this paper, we propose eigen decomposition-precoded faster-than-Nyquist (FTN) signaling with power allocation in a frequency-selective fading channel. More specifically, we derive mutual information associated with the proposed FTN signaling. Then, the optimal power coefficients are calculated such that the derived mutual information is maximized. Our analytical performance results show that the proposed FTN signaling scheme achieves a higher information rate than the conventional FTN signaling scheme without relying on power allocation and the classic Nyquist-based signaling scheme, under the assumption that all the schemes employ a root-raised cosine shaping filter. Moreover, our numerical simulation results of the bit error ratio performance and the power spectral density demonstrate that the proposed FTN scheme outperforms the conventional Nyquist-based signaling scheme without sacrificing any bandwidth broadening.<br></p><p><br></p><p>Postprint accepted on 24 March 2021 for publication in IEEE International Conference on Communications Workshops (ICC Workshops), June 2021.</p>

scholarly journals Precoded Faster-than-Nyquist Signaling with Optimal Power Allocation in Frequency-Selective Channel

2021 ◽  
Author(s):  
Takumi Ishihara ◽  
Shinya Sugiura
Keyword(s):  
Mutual Information ◽  
Power Allocation ◽  
Shaping Filter ◽  
Optimal Power ◽  
Power Spectral ◽  
Bit Error Ratio ◽  
Selective Channel ◽  
Frequency Selective ◽  
Eigen Decomposition ◽  
Performance Results

<p>In this paper, we propose eigen decomposition-precoded faster-than-Nyquist (FTN) signaling with power allocation in a frequency-selective fading channel. More specifically, we derive mutual information associated with the proposed FTN signaling. Then, the optimal power coefficients are calculated such that the derived mutual information is maximized. Our analytical performance results show that the proposed FTN signaling scheme achieves a higher information rate than the conventional FTN signaling scheme without relying on power allocation and the classic Nyquist-based signaling scheme, under the assumption that all the schemes employ a root-raised cosine shaping filter. Moreover, our numerical simulation results of the bit error ratio performance and the power spectral density demonstrate that the proposed FTN scheme outperforms the conventional Nyquist-based signaling scheme without sacrificing any bandwidth broadening.<br></p><p><br></p><p>Postprint accepted on 24 March 2021 for publication in IEEE International Conference on Communications Workshops (ICC Workshops), June 2021.</p>

scholarly journals Filter Bank Multi Carrier Signal System for Frequency Selective Channels

2021 ◽  
Vol 38 (2) ◽  
pp. 413-420
Author(s):  
Sarala Patchala ◽  
Sailaja Maruvada
Keyword(s):  
Filter Bank ◽  
Fourier Transforms ◽  
Mimo System ◽  
Discrete Fourier Transforms ◽  
Power Spectral ◽  
Filter Bank Multicarrier ◽  
Narrow Frequency Band ◽  
Selective Channel ◽  
Spectral Control ◽  
Frequency Selective

Filter Bank Multicarrier (FBMC) frameworks are a subclass of multicarrier (MC) frameworks. The essential guideline, separating spectrum into many thin sub channels, may not be new, MC frameworks have seen wide appropriation. These days, multicarrier regulation frameworks dependent on the discrete Fourier transforms are usually used to transmit over recurrence particular channels subject to forceful noise aggravations. In any case, these handsets experience the ill effects of poor sub channel spectral control, that is, the measure of inter channel impedance isn't unimportant. It very well may be indicated that the framework execution reduces when it is dependent upon an unsettling influence with a large portion of its energy focused on a narrow frequency band. This Paper aims that identify the Filter Bank Multi Carrier (FBMC) performance. The MIMO system combined with the FBMC then identifies the over Frequency Selective Channel (FSC). Here the analysis for FSC, Flat fading model FBMC and system with MMSE equalization. The Prototype filters are analyzing the system performance characteristics. The Power Spectral Density (PSD) of the MIMO FBMC system for the given spectrum. The proposed systems are best to compare all existing technique and we measure the spectral efficiency of the system.

scholarly journals Capacity of Data Collection in Wireless Sensor Networks Based on Mutual Information and MMSE Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ajib Setyo Arifin ◽  
Tomoaki Ohtsuki
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Mutual Information ◽  
Power Allocation ◽  
Wireless Sensor ◽  
Total Power ◽  
Multiple Access Channel ◽  
Optimal Power ◽  
Equal Power

We investigate the properties of data collection in wireless sensor networks, in terms of both capacity and power allocation strategy. We consider a scenario in which a number of sensors observe a target being estimated at fusion center (FC) using minimum mean-square error (MMSE) estimator. Based on the relationship between mutual information and MMSE (I-MMSE), the capacity of data collection in coherent and orthogonal multiple access channel (MAC) models is derived. Considering power constraint, the capacity is derived under two scenarios: equal power allocation and optimal power allocation of both models. We provide the upper bound of capacity as a benchmark. In particular, we show that the capacity of data collection scales as Θ((1/2)log(1+L)) when the number of sensors L grows to infinity. We show through simulation results that for both coherent and orthogonal MAC models, the capacity of the optimal power is larger than that of the equal power. We also show that the capacity of coherent MAC is larger than that of orthogonal MAC, particularly when the number of sensors L is large and the total power P is fixed.

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