Receiver Side Research Articles

Combination of three‐coil and LCC‐S structures for misalignment tolerance in wireless charging system with load‐independent output

SummaryMisalignment tolerance is an important issue for the electric vehicle wireless charging system. This letter proposes a hybrid topology of the inductor–capacitor–capacitor‐series (LCC‐S) and three‐coil structures with load‐independent output to achieve misalignment tolerance, which is suitable for anti‐offset in the door‐to‐door direction of electric vehicles. The unipolar coil is divided into two windings to form the two coils in the three‐coil structure. The bipolar coil is employed as the transmitting coil of the LCC‐S topology, realizing decoupling from the two unipolar windings of three‐coil structure. The receiving coil only consists of a unipolar coil. From the central to the boundary positions, the output power of the three‐coil structure decreases while that of the LCC‐S topology increases. In this way, the output power can be stabilized against misalignment. Compared with existing hybrid topologies with two sets of coils, the receiver side adopts only one receiving coil and one compensation, which is compact, low‐profile, and cost effective. Besides, the output voltage is independent of the load, and the system can achieve constant voltage output characteristic. The experimental results validate the proposed system.

Secure message recovery in presence of the cyber-attack using discrete-time chaotic modulation approach

The secure message recovery problem in presence of cyber-attack is considered in this paper. The message is modulated by a master chaotic system which may be vulnerable to a cyber-attack and then transmitted to a receiver. By designing a novel observer at the receiver side, the message is recovered correctly. All the design process is performed in discrete-time domain which is suitable for digital communications. Besides, there are no restrictions on the message signal and nonlinear term signature matrix which makes the proposed approach more suitable in the real applications. Numerical simulations are introduced to show the feasibility and the superior performance of the proposed scheme in comparison with the conventional methods.

Improved End-to-End Data Security Approach for Cloud Computing

Cloud computing is one of the major cutting-edge technologies that is growing at a gigantic rate to redefine computation through service-oriented computing. It has addressed the issue of owning and managing computational infrastructure by providing service through a pay-and-use model. However, a major possible hindrance is security breaches, especially when the sender uploads or the receiver downloads the data from a remotely accessed server. It is a very generic approach to ensuring data security through different encryption techniques, but it might not be able to maintain the security standard. This paper proposes an end-to-end data security approach from the sender side to the receiver side by adding extra padding sequences, as well as randomized salting, followed by hashing and an encryption technique. The effectiveness of the proposed method was established using both a simulated system and mathematical formulations with different performance metrics. Furthermore, its performance was compared with those of contemporary algorithms, showing that the proposed algorithm creates a larger ciphertext that is almost impossible to crack due to randomization modules. However, it has significantly longer encryption and decryption times, although our primary concern is ensuring security, not reducing time.

Linear-in-dB Logarithmic Signal Strength Sensor Circuit for Wireless Power Transfer Receivers

Tracking systems for wireless power transfer are becoming a necessity. The received target signals are sometimes weak, which is why it is critical to have a dedicated received–signal–strength indicator (RSSI) for signal detection. It can also be used for transmitter localization and automatic gain control (AGC) to ensure continuous coverage. In this paper, a logarithmic detector coupled with a high-speed full-wave rectifier is designed for an RSSI system. The aim is to place the RSSI on the receiver side of unmanned aerial vehicles used, for example, as agricultural drones. This system is intended to operate in a scenario where multiple drones in a smart farm are charged wirelessly whilst airborne. An RSSI is placed in the receiver to detect and track wireless signals and to ensure the drone is charged while in motion. The RSSI system operates at 5.8 GHz, reported for the first time, and is capable of detecting signal strengths from −60 dBm to 0 dBm with a sensitivity level of 17 mV/dBm. A logarithmic error of 0.4 dB with a dynamic range of 34 dB was achieved. The proposed RSSI system was designed using 65 nm TSMC CMOS technology, and it exhibits high sensitivity, better efficiency, and lower power consumption than those in other reported works.

Iterative Equalization and Decoding over an Additive White Gaussian Noise Channel with ISI Using Low-Density Parity-Check Codes

In this article we present an iterative system of equalization and decoding to manage the intersymbol interference over an additive white Gaussian noise (AWGN) channel. Following the classic turbo equalization scheme, the proposed system consists of low-density parity-check (LDPC) coding at the transmitter side; we applied a Log maximum a posteriori probability (Log-MAP) equalizer and min-sum LDPC decoding at the receiver side. The equalizer and decoder, linked through interleaving and deinterleaving, iteratively update each other’s information. We performed the performance analysis of the proposed system, bit error rate (BER) vs. signal-to-noise ratio (SNR), considering three different impulse responses of the channel (h). Our experimental results indicated that increasing the number of iterations performed by the LDPC decoder from 10 to 20 during the iterative process of equalization and decoding leads to better outcomes. The proposed system was compared with turbo equalization and separate equalization, performed before the decoding process with minimum mean-square error (MMSE) and LDPC decoding, in terms of BER vs. SNR, considering the three different h. Based on the analyzed results, it can be concluded that the equalization performance depends on both the impulse responses of the channel and the chosen decoding and equalization method; therefore, the equalization method does not always offer good results for any h.

A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm.

In wireless communication, multiple signals are utilized to receive and send information in the form of signals simultaneously. These signals consume little power and are usually inexpensive, with a high data rate during data transmission. An Multi Input Multi Output (MIMO) system uses numerous antennas to enhance the functionality of the system. Moreover, system intricacy and power utilization are difficult and highly complicated tasks to achieve in an Analog to Digital Converter (ADC) at the receiver side. An infinite number of MIMO channels are used in wireless networks to improve efficiency with Cross Entropy Optimization (CEO). ADC is a serious issue because the data of the accepted signal are completely lost. ADC is used in the MIMO channels to overcome the above issues, but it is very hard to implement and design. So, an efficient way to enhance the estimation of channels in the MIMO system is proposed in this paper with the utilization of the heuristic-based optimization technique. The main task of the implemented channel prediction framework is to predict the channel coefficient of the MIMO system at the transmitter side based on the receiver side error ratio, which is obtained from feedback information using a Hybrid Serial Cascaded Network (HSCN). Then, this multi-scaled cascaded autoencoder is combined with Long Short Term Memory (LSTM) with an attention mechanism. The parameters in the developed Hybrid Serial Cascaded Multi-scale Autoencoder and Attention LSTM are optimized using the developed Hybrid Revised Position-based Wild Horse and Energy Valley Optimizer (RP-WHEVO) algorithm for minimizing the "Root Mean Square Error (RMSE), Bit Error Rate (BER) and Mean Square Error (MSE)" of the estimated channel. Various experiments were carried out to analyze the accomplishment of the developed MIMO model. It was visible from the tests that the developed model enhanced the convergence rate and prediction performance along with a reduction in the computational costs.

Dual polarized optical multi‐input multi‐output transmission over 7‐core few‐mode fiber using adaptive recursive least squares constant modulus algorithm

AbstractWe describe a system with multi‐input multi‐output (MIMO) transmission over 7‐core few‐mode fiber (FMF) by incorporating polarization division multiplexing and mode division multiplexing. Three optical modes with x and y polarizations were used for 42 × 42 MIMO transmission over 7‐core FMF. We simulate the optical MIMO transmission at 20 Gbps with quadrature phase‐shift keying signals. By using the adaptive recursive least squares constant modulus algorithm (RLSCMA) at the receiver side, transmission data is successfully recovered by mitigating the effects of modal delay and mode coupling of received signals. The simulation results show that adaptive RLSCMA can improve the system performance of optical MIMO transmission systems.

Simplified Deep Reinforcement Learning Approach for Channel Prediction in Power Domain NOMA System.

In this work, the impact of implementing Deep Reinforcement Learning (DRL) in predicting the channel parameters for user devices in a Power Domain Non-Orthogonal Multiple Access system (PD-NOMA) is investigated. In the channel prediction process, DRL based on deep Q networks (DQN) algorithm will be developed and incorporated into the NOMA system so that this developed DQN model can be employed to estimate the channel coefficients for each user device in NOMA system. The developed DQN scheme will be structured as a simplified approach to efficiently predict the channel parameters for each user in order to maximize the downlink sum rates for all users in the system. In order to approximate the channel parameters for each user device, this proposed DQN approach is first initialized using random channel statistics, and then the proposed DQN model will be dynamically updated based on the interaction with the environment. The predicted channel parameters will be utilized at the receiver side to recover the desired data. Furthermore, this work inspects how the channel estimation process based on the simplified DQN algorithm and the power allocation policy, can both be integrated for the purpose of multiuser detection in the examined NOMA system. Simulation results, based on several performance metrics, have demonstrated that the proposed simplified DQN algorithm can be a competitive algorithm for channel parameters estimation when compared to different benchmark schemes for channel estimation processes such as deep neural network (DNN) based long-short term memory (LSTM), RL based Q algorithm, and channel estimation scheme based on minimum mean square error (MMSE) procedure.

Ultra high reach optical fiber systems based on CW laser and VCSEL laser operation performance signature

Abstract This study presented the possibility of the hybrid cooperation between continuous wave laser (CW) laser and vertical cavity surface emitting laser (VCSEL) laser operation performance signature for upgrading ultra high reach optical fiber systems. The total estimated lighted power spectrum configuration is simulated with wavelength ranges from 1.543 to 1.555 μm. Total estimated lighted power spectrum configuration is studied with spectral time variations. Total estimated lighted power spectrum is indicated and measured after fiber system length. The total electrical modulated signal amplitude spectrum variations are demonstrated with threshold value after receiver side. The total signal/noise modulated spectrum signal configuration is clarified with time after receiver side. Total estimated electronic power spectrum is measured and tested after receiver system. Also, the total estimated electronic power spectrum configuration is demonstrated with spectral frequency ranges from 200 to 800 GHz.

Optimized Precoding for MU-MIMO With Fronthaul Quantization

One of the first widespread uses of multi-user multiple-input multiple-output (MU-MIMO) is in 5G networks, where each base station has an advanced antenna system (AAS) that is connected to the baseband unit (BBU) with a capacity-constrained fronthaul. In the AAS configuration, multiple passive antenna elements and radio units are integrated into a single box. This paper considers precoded downlink transmission over a single-cell MU-MIMO system. We study optimized linear precoding for AAS with a limited-capacity fronthaul, which requires the precoding matrix to be quantized. We propose a new precoding design that is aware of the fronthaul quantization and minimizes the mean-squared error at the receiver side. We compute the precoding matrix using a sphere decoding (SD) approach. We also propose a heuristic low-complexity approach to quantized precoding. This heuristic is computationally efficient enough for massive MIMO systems. The numerical results show that our proposed precoding significantly outperforms quantization-unaware precoding and other previous approaches in terms of the sum rate. The performance loss for our heuristic method compared to quantization-aware precoding is insignificant considering the complexity reduction, which makes the heuristic method feasible for real-time applications. We consider both perfect and imperfect channel state information (CSI).

Tanner-Graph-Based Massive Multiple Access—Transmission and Decoding Schemes

In this paper we consider two Tanner-graph-based transmission schemes for massive multiple access, which combine non-orthogonal multiple access (NOMA) and grant-based random access. Each user transmits two data streams repeatedly using several channel time-frequency resource blocks (RBs) and the transmission schedule is represented by a Tanner graph, where variable nodes and check nodes represent the transmitted signals and the RBs, respectively. In Scheme 1 each variable node represents a data stream of a user, whereas Scheme 2 employs rate splitting and each variable node represents the superimposed data streams of a user. On the receiver side, we first consider peeling decoders that serve both as pilot-based channel estimators and baseline decoders. We then develop message-passing decoders for both transmission schemes that can fully exploit the diversity afforded by the repetitive transmission across different RBs, as opposed to the peeling decoders. We also propose a neural decoder by deep unfolding the message-passing decoder and further performing a small number of training epochs using the pilots. Simulation results show that for Transmission Scheme 1, the message-passing decoder offers decoding performance improvement over the peeling decoder by orders of magnitude; and as a result, Scheme 1 substantially outperforms Scheme 2. Moreover, the neural decoder further improves upon the performance of the message-passing decoder, as more information is learned about the transmitted data over the training epochs.

Wireless Animatronic Hand

As a result of employees utilizing their bare hands, there are more accidents in industries every year. Animatronic hands resemble human hands and are capable of carrying out a variety of tasks that humans are unable to do. Human hands cannot readily finish difficult tasks, but animatronic hands can mimic human movements and complete them. A number of actuators and sensors make up the robotic hand, which are managed by a person's hand. The pushers control how far the fingers can move. We employ flex sensors to detect the positions. The positions and motion of the hand are reported by sensors to Arduino. The microcontroller uses a 433MHz transmitter and receiver in both transmitter and receiver side to used for transmitting the data, so it will be access in remote area. We employ servo motors as actuators by getting the position and motion from another microcontroller also attached. Two microcontrollers are interconnected with 433MHz transmitter and receiver, Transmitter and receiver for 433 MHz When sending and receiving radio signals between any two devices, RF refers to a set of electronic RF transmitters and receiver modules. The transmitter module at the transmitter end transmits the data, which the receiver module at the receiver end receives.

Performance Evaluation of Conventional and Neural Network-Based Decoder for an Audio of Low-Girth LDPC Code

Noise in a communication system degrades the signal level at the receiver, and as a result, the signal is not properly recovered or eliminated at the receiver side. To avoid this, it is necessary to modify the signal before transmission, which is achieved using channel coding. Channel coding provides an opportunity to recover the noisy signal at the receiver side. The low-density parity-check (LDPC) code is an example of a forward error correcting code. It offers near Shannon capacity approaching performance; however, there is a constraint regarding high-girth code design. When the low-girth LDPC code is decoded using conventional methods, an error floor can occur during iterative decoding. To address this issue, a neural network (NN)-based decoder is utilized to overcome the decoding problem associated with low-girth codes. In this work, a neural network-based decoder is developed to decode audio samples of both low- and high-girth LDPC codes. The neural network-based decoder demonstrates superior performance for low-girth codes in terms of bit error rate (BER), peak signal-to-noise-ratio (PSNR), and mean squared error (MSE) with just a single iteration. Audio samples sourced from the NOIZEUS corpus are employed to evaluate the designed neural network. Notably, when compared to a similar decoder, the decoder developed in this study exhibits an improved bit error rate for the same signal-to-noise ratio.

High modulation laser systems integrated with hybrid pulse generators for high speed fiber transmission systems

Abstract This study has simulated the high modulation laser systems integrated with hybrid pulse generators for high speed fiber transmission systems. The directly modulated laser with raised cosine/Gaussian pulse generators are employed for ultra-high data rates for modified multi band range applications. The total estimated light power after fiber cable system is demonstrated based on electro-absorption, phase, and single drive MZM measured modulators. The light base band signal and noise signal configuration is clarified after fiber cable system based on various proposed modulators. The degree of signal polarization configuration is studied after fiber cable system based on various modulators. Signal power level with Q, BER, signal eye height, and the total estimated electrical modulated signal power are evaluated after receiver system after receiver side based on proposed modulators.

Border Collie Shuffled Shepherd optimization-based image reconstruction using visual cryptography

ABSTRACT The visual secret sharing (VSS) technique is an encryption model to conceal secret messages into two or more meaningless images, called shares. Information loss is a huge drawback in existing systems. An efficient model has been developed to reconstruct the original image using the Border Shuffled Shepherd Optimization (BSSO) based on the visual cryptography technique. Visual cryptography is performed on the transmitter and receiver sides. At the transmitter side, the input image is partitioned into numerous shares and the B-noisy matrix is optimally generated using BSSO, which is an incorporation of Border Collie Optimization (BCO) and Shuffled Shepherd Optimization (SSO). At the receiver end, all the shares are integrated to reconstruct the original image. The BSSO has maximum Peak-to-Signal Noise Ratio (PSNR) of 56.591, distortion ratio of 0.908, noise level of 0.834, Mean squared error (MSE) of 0.038, and Structural Similarity Index (SSIM) of 0.981, for Lena image.

Robust voltage-controlled transcutaneous energy transfer system for artificial anal sphincter.

The artificial anal sphincter (AAS) system has gained significant attention as a solution for treating fecal incontinence (FI). It relies on transcutaneous energy transfer (TET) as its primary energy source. However, changes in posture or biological tissue can cause misalignment of the coil, resulting in unstable power reception. Inadequate power affects charging efficiency, while excessive power leads to excessive heating at the receiver side. Consequently, achieving safe and constant voltage charging for the AAS becomes a complex challenge. To maintain a consistent charging voltage and overcome the issue of variations in load and coil coupling strength, this article proposes a wireless charging control system that utilizes an LCC-S-type resonant network and phase shift to adjust the transmitting voltage based on feedback charging voltage in real time. In particular, the PI controller and neural network are introduced to change the phase-shift angle swiftly. The dynamic performance is then evaluated under different misalignments and presented with comparative results. The results indicate that the multilayer perceptron control system outperforms the PI. Under the complex misalignment disturbance, the average error of receiver side load voltage is only 0.007 V, with an average settling time of 960 ms. Additionally, the average temperature at the receiver side is 40.4°C. The experiments demonstrate that the proposed system effectively addresses the misalignment issue in TET during the charging, ensuring constant voltage charging at the receiver side and thermal safety.

BRD_ESRNet and SRS-Based Channel Estimation

In wireless communication, the channel function estimated commonly has errors due to the influence of noise, so traditional channel estimation methods cannot accurately estimate the real channel function. Aiming at this problem, we propose a channel estimation method that combines sounding reference signal (SRS) remapping with the deep-learning network BRD_ESRNet. BRD_ESRNet consists of image denoising using a deep convolutional neural network with batch renormalization (BRDNet) and an expanded superresolution convolutional neural network (ESRCNN). At the transmitter side, we first map the SRS into four-box structures, and then, the four-box structures are scattered distribution throughout the time-frequency resource block. At the receiver side, we first perform the modified least squares (LS) estimation based on the four-box structure and place the result into the top-left resource unit of the four box. Then, we perform linear interpolation for the whole resource block. Finally, we equate the estimated channel matrix to a low-resolution image containing noise and input it to BRD_ESRNet. Thus, we obtain data with high resolution and achieve the purpose of reducing the estimation error of the channel function. The experimental results show that the proposed method in this paper has a significant improvement in performance compared to the methods of Soltani et al. and Nithya et al. In this paper, the methods of Soltani et al. and Nithya et al. are referred to as methods 1 and 2, respectively.

A Misalignment-Tolerant Hybrid Coupler for Electric Vehicle IPT Charging Systems

To solve the problems of output voltage and system efficiency sharply decreasing in the case of misalignment for the electric vehicle inductive power transfer (IPT) charging system, a universal hybrid coupler with high misalignment tolerance is proposed. The hybrid coupler consists of a four-coil bipolar (BP) coupler, the T-type network and a compensation capacitor, which effectively achieves high anti-misalignment for any compensation networks. The introduction of the T-type network makes the two overlapping coils in the same side of the BP coupler transmitter decouple effectively and simplifies the design. Although the two overlapping coils in the receiver side are still coupled with each other, the parameter design only needs to consider the cross-coupling to be small enough, which significantly reduces the design difficulty of the coupler without iteration and repetition. Finally, a 2-kW misalignment-tolerance hybrid IPT system is constructed based on the proposed hybrid IPT coupler. A configurable series-series (SS) and series/inductor-capacitor- capacitor(S/LCC) compensation topologies are used to implement constant current (CC) and constant voltage (CV) load-independent outputs in the face of conditions where the coupling coefficient and load change simultaneously. The experimental results validate the correctness of the theoretical analysis.

High-Resolution Indoor Sensing Using Channel State Information of WiFi Networks

Indoor sensing is becoming increasingly important over time as it can be effectively utilized in many applications from digital health care systems to indoor safety and security systems. In particular, implementing sensing operations using existing infrastructures improves our experience and well-being, and exhibits unique advantages. The physical layer channel state information for wireless fidelity (WiFi) communications carries rich information about scatters in the propagation environment; hence, we exploited this information to enable detailed recognition of human behaviours in this study. Comprehensive calibration and filtering techniques were developed to alleviate the redundant responses embedded in the channel state information (CSI) data due to static objects and accidental events. Accurate information on breathing rate, heartbeat and angle of arrival of the incoming signal at the receiver side was inferred from the available CSI data. The method and procedure developed can be extended for sensing or imaging the environment utilizing wireless communication networks.

Small-angle measurement system enhanced byan optical phased array.

Small-angle measurement can be realized by embedding the laser beam in a reflective sector, within which multiple reflections enlarge the angle between the input and the output beams. However, the maximum detectable angle is limited by the detector aperture at the receiver side. In this work, we propose, to the best of our knowledge, a novel small-angle measurement system enhanced by an optical phased array (OPA), which is loaded on a spatial light modulator (SLM) to increase the maximum measurement range. The experimental results verify the effectiveness of the proposed system, and a wider measurement range with an unaffected measurement accuracy can be obtained. In the proof-of-concept demonstration, the measurement range of the system is enlarged by at least five times compared to the system without OPA, while maintaining the same measurement accuracy.