Link Quality Estimation of Cross-Technology Communication: The Case with Physical-Level Emulation

Research on cross-technology communication ( CTC ) has made rapid progress in recent years. While the CTC links are complex and dynamic, how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. Furthermore, the channel distortion can be modeled and observed through the signal strength and the noise strength. We, in this article, propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the In-phase and Quadrature ( IQ ) domain. We accurately describe the superimposed impact on the received signal. We further design a lightweight link estimation approach including two different methods to estimate C-LQI and in turn the packet reception rate ( PRR ) over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative estimation error by 49.8% and 51.5% compared with s-PRR and EWMA, respectively.

A Real-time Implementation for the Speech Steganography using Short-Time Fourier Transformior Secured Mobile Communication

Steganography can be described as approach of masking an undisclosed message with a normal message which is known as the Carrier message signal. DSP techniques, such as LSB encoding, have historically been implemented for secret information hiding. Utilization ofsteganography functions of deep neural networks for voice data is something this paper will present. This paper also demonstrate that the steganography techniques suggested for vision are less suitable for speech signals this paper present a implementation technique that involves the use of ISTFT and STFT as differentiablelayers in the network. Empirically, the efficacy of the proposed methods based on multiple datasets of speech should be demonstrated and the outcome are examined quantitatively and qualitatively. Using of multiple decoders or a single conditional decoder helps to hide multiple signals in a single carrier signal. Finally, under various channel distortion situations, this model Qualitative studies indicate that human listeners cannot detect changes made to the carrier and hence the decoded messages are highly intelligible.

Transmission Strategy for Simultaneous Wireless Information and Power Transfer with a Non-Linear Rectifier Model

Most studies determining data rate or power conversion efficiency (PCE) of simultaneous wireless information and power transfer (SWIPT) focus on ideal models for the non-linear energy harvester, or focus on simplified waveforms that carry no information. In this paper, we study SWIPT using realistic waveforms and a measurement-based energy harvesting model. For a special class of multisine waveforms carrying only information in the phase, we analyze PCE as a function of waveform design, including the impact of pre-equalization to mitigate wireless channel distortion. A balanced pre-equalizer that trades off between the peak-to-average power ratio (PAPR) and signal to noise ratio, maximizing the total PCE is proposed. The impact on the information rate of the analyzed waveforms is also presented. The results show that balanced pre-equalizers can improve the total PCE more than three times within 5% rate loss compared to the pre-equalizer that solely maximizes the signal PAPR or the capacity using the same transmit power. We also show that the maximum normalized PCE is increased by a factor of two by only allowing phase modulation to ensure the PAPR of one symbol, compared to traditional modulation schemes that carry information in both phase and amplitude to maximize spectral efficiency.

Cost Effectual Distributed Cooperative Cluster based Communication Protocol in Wireless Sensor Network

Theoretical energy recognition in remote sensor systems has received intense research interest in the late years. Radio variation, channel distortion, and blockage bring great strength and responsiveness to packets broadcast over a remote channel. A twin innovation is effective communication that can drastically increase the channel range and reduce transmission vigor consumption in disrupting channel. Growth in the direct range brings with it a reduced fault rate. In this paper, an acceptable correspondence method is proposed for each tab with active sending and receiving clusters. It consists of two stages, the precise routing phase, the selective and transmitting stage. In the routing phase, the basic route between the source and the sink hub is started. In the second stage, centers of fundamental development toward flattering team leaders select additional touch centers with minimal biomass costs from their surroundings, and then spread from bundle to cluster to the recently established endurance cluster. Reductions in error rate and regeneration are proven by the fact that malpractice funds become long-term obligation systems.

Channel Estimation and Signal Detection in OFDM Systems using Deep Learning

this article presents “channel estimation and signal detection in OFDM systems by using deep learning”. OFDM stands for “Orthogonal Frequency Division Multiplexing”. This paper exploits end to end handling of wireless OFDM channels by deep learning. It is different from the existing OFDM receivers as it estimates the channel state information (CSI) explicitly and then estimated CSI is used to recover the transmitted symbols, thee proposed approach of deep learning implicitly estimates CSI and the transmitted symbols are recovered directly. The online transmitted data is directly recovered by the offline training a deep learning model using simulation based channel statistics generated data for addressing channel distortion. The performance comparable to “minimum mean square error” (MSME) estimator with transmitted symbols is detected by using deep learning based channel distortion. Using fewer number of pilots, omitting cyclic prefix and in the existence of nonlinear clipping noise, the approach of deep learning is more robust as compared to traditional methods.

Using Least Mean p-Power Algorithm to Correct Channel Distortion in MC-CDMA Systems

This work focuses on adaptive Broadband Radio Access Network (BRAN) channel identification and on downlink Multi-Carrier Code Division Multiple Access (MCCDMA) equalization. We use the normalized BRAN C channel model for 4G mobile communications, distinguishing between indoor and outdoor scenarios. On the one hand, BRAN C channel parameters are identified using the Least Mean p-Power (LMP) algorithm. On the other, we consider these coefficients in the context of adaptive equalization. We provide an overview and a mathematic formulation of MC-CDMA systems. According to these fundamental concepts, the equalizer technique is investigated analytically to compensate for channel distortion in terms of the bit error rate (BER). The numerical simulation results, for various signal-to-noise ratios and different p threshold, show that the presented algorithm is able to simulate the BRAN C channel measured with different accuracy levels. Furthermore, as far as the adaptive equalization problem is concerned, the results obtained using the zero-forcing equalizer demonstrate that the algorithm is adequate for some particular cases of threshold p.