Improved Bit Error Rate Research Articles

A Hybrid Network Integrating MHSA and 1D CNN–Bi-LSTM for Interference Mitigation in Faster-than-Nyquist MIMO Optical Wireless Communications

To mitigate inter-symbol interference (ISI) caused by Faster-than-Nyquist (FTN) technology in a multiple input multiple output (MIMO) optical wireless communication (OWC) system, we propose an ISI cancellation algorithm that combines multi-head self-attention (MHSA), a one-dimensional convolutional neural network (1D CNN), and bi-directional long short-term memory (Bi-LSTM). This hybrid network extracts data features using 1D CNN and captures sequential information with Bi-LSTM, while incorporating MHSA to comprehensively reduce ISI. We analyze the impact of antenna numbers, acceleration factors, wavelength, and turbulence intensity on the system’s bit error rate (BER) performance. Additionally, we compare the waveform graphs and amplitude–frequency characteristics of FTN signals before and after processing, specifically comparing sampled values of four-pulse-amplitude modulation (4PAM) signals with those obtained after ISI cancellation. The simulation results demonstrate that within the Mazo limit for selecting acceleration factors, our proposal achieves a 7 dB improvement in BER compared to the conventional systems without deep learning (DL)-based ISI cancellation algorithms. Furthermore, compared to systems employing a point-by-point elimination adaptive pre-equalization algorithm, our proposal exhibits comparable BER performance to orthogonal transmission systems while reducing computational complexity by 31.15%.

Asynchronous Code Division Multiplexing-Based Visible Light Positioning and Communication Network Using Successive Interference Cancellation Decoding.

In the evolving landscape of sixth-generation wireless communication, the integration of visible light communication (VLC) and visible light positioning (VLP), known as visible light positioning and communication (VLPC), emerges as a pivotal technology. This study addresses the challenges of asynchronous code division multiplexing (ACDM) in VLPC networks, focusing on the enhancement of data transmission quality and positioning accuracy. Firstly, we propose an orthogonal pseudo-random code (OPRC) for ACDM-based VLP systems. Leveraging its excellent correlation properties, VLP signals preserve orthogonality even amidst asynchronous transmissions, achieving sub-centimeter average positioning errors. Next, by combining OPRC with successive interference cancellation decoding (SICD), we propose an enhanced ACDM-based VLPC system that utilizes OPRC for improved signal orthogonality and SICD for progressive elimination of multiple access interference (MAI) among VLPC signals. The results show substantial improvements in bit-error rate (BER) and positioning error (PE), approaching the performance levels observed in synchronized VLPC systems. Specifically, the SICD-OPRC scheme reduces average BER to 4.3 × 10-4 and average PE to 2.7 cm, demonstrating its robustness and superiority in complex asynchronous scenarios.

AI-DRIVEN TRANSFORMER NETWORKS IN SHARED SPECTRUM FOR ENHANCED SIGNAL PROCESSING FOR NONLINEAR RECEIVERS

Communication systems face challenges from high-power adjacent channel signals, or blockers, inducing nonlinear behavior in RF front ends. Ensuring robust performance in the presence of blockers is crucial for IoT and other spectrum-consuming devices coexisting with advanced transceivers. This paper proposes a flexible, data-driven solution using a Deep Belief Network (DBN) to mitigate third-order intermodulation distortion (IMD) during demodulation. Numerical evaluations of AI-enhanced receivers employing DBN as an IMD canceler and demodulator show significant improvements in bit error rate (BER) performance. The effectiveness of DBN varies with RF front end characteristics, notably the third-order intercept point (IP3).

Demonstration of D-band 1×2 SIMO millimeter-wave wireless delivery over 1.2 km employing MRC technology

We experimentally demonstrate the transmission of 20-Gbaud QPSK millimeter-wave signals within a 1.2-km wireless range at 128-GHz using constant-modulus-algorithm (CMA) and the maximum ratio combining (MRC) technology with over 2.2-dB gain in a photonics-assisted millimeter wave (MMW) 1 × 2 Single Input Multi Output (SIMO) communication system. MRC is applied for SIMO signal merging which leads to Signal to Noise Ratio (SNR) enhancement and significant Bit Error Rate (BER) reduction. The comparison which is performed for Single Input Single Output (SISO) and SIMO system, demonstrates the BER improvement. To the best of our knowledge, this is the first experimental demonstration of high gain in a D-band MMW 1 × 2 SIMO system by using these technologies.

Novel Approach towards a Fully Deep Learning-Based IoT Receiver Architecture: From Estimation to Decoding

As the Internet of Things (IoT) continues to expand, wireless communication is increasingly widespread across diverse industries and remote devices. This includes domains such as Operational Technology in the Smart Grid. Notably, there is a surge in resource-constrained devices leveraging wireless communication, especially with the advances of 5G/6G technology. Nevertheless, the transmission of wireless communications demands substantial power and computational resources, presenting a significant challenge to these devices and their operations. In this work, we propose the use of deep learning to improve the Bit Error Rate (BER) performance of Orthogonal Frequency Division Multiplexing (OFDM) wireless receivers. By improving the BER performance of these receivers, devices can transmit with less power, thereby improving IoT devices’ battery life. The architecture presented in this paper utilizes a depthwise Convolutional Neural Network (CNN) for channel estimation and demodulation, whereas a Graph Neural Network (GNN) is utilized for Low-Density Parity Check (LDPC) decoding, tested against a proposed (1998, 1512) LDPC code. Our results show higher performance than traditional receivers in both isolated tests for the CNN and GNN, and a combined end-to-end test with lower computational complexity than other proposed deep learning models. For BER improvement, our proposed approach showed a 1 dB improvement for eliminating BER in QPSK models. Additionally, it improved 16-QAM Rician BER by five decades, 16-QAM LOS model BER by four decades, 64-QAM Rician BER by 2.5 decades, and 64-QAM LOS model BER by three decades.

Performance analysis of massive MIMO using various transmit precoding schemes for a wireless network

SummaryAn evaluation of various transmit precoding schemes is proposed in this thesis on basis of spectral efficiency (SE) of massive MIMO (m‐MIMO) system in 2 GHz spectrum. The present generation of wireless networks must be rethought due to the rapidly rising user base, huge volume data transfer, wide coverage requirement, and exponential rise in data rate. 5G mobile network includes a multi‐fold increase in network capacity, energy conservation, decreased latency, higher coverage probability, and a seamless connection. m‐MIMO has provided good solution for enhancing the data rate with the coverage, overcoming the effects of multipath fading and has also increased diversity of the system. Massiveness of MIMO system along with 5G network is the key technology to address the issue of SE, throughput, improvement of QoS (quality of service), coverage probability, power delay profile, diversity simulation, energy efficiency, and improvement in bit error rate. Parameters such as SE, throughput, and coverage probability are important for any communication system, and their improvement can increase overall capacity of cellular network system and could meet the requirement of upcoming demand for mobile network. The research paper is focused on performance analysis of m‐MIMO with reference to the SE of various transmit precoding schemes. Simulation results show that SE provided by M‐MMSE (multi‐cell minimum mean‐squared error) technique of precoding ranks highest for the same number of antennas and pilot reuse factor as compared to other schemes.

Evaluating Performance: A Study of Encrypted vs. Unencoded Signals in SISO-OFDM with LS and MMSE Estimations

The continuous progress in digital communication has played a crucial role in meeting the increasing need for faster data rates. Orthogonal Frequency Division Multiplexing (OFDM), a pivotal methodology in this progression, attains improved data rates through the efficient utilisation of densely packed carriers within a specified channel bandwidth. This article focuses on the investigation of channel estimation in OFDM systems. the study of search lies in its examination of the performance consequences associated with the incorporation or lack thereof of a convolutional encoder in OFDM systems and looks at how well two well-known channel estimation algorithms, Least Square (LS) and Minimum Mean Square Error (MMSE), work in 4-Quadrature amplitude modulation (4-QAM) OFDM systems with and without a convolutional encoder with a comprehensive evaluation of the efficacy of the OFDM system across various channel conditions. It uses MATLAB implementations as its main tool. The findings of the study indicate that the MMSE algorithm, despite its higher complexity, exhibits superior performance in comparison to the LS algorithm when combined with a convolutional encoder. The gain in terms of bit error rate (BER) improvement approximately 12 dB. This represents the logarithmic scale improvement in BER from BER1(uncoded) to BER2(coded) at the same the energy per bit to noise power spectral density ratio (Eb/N0) of 40 dB.

A mathematical analysis of the PAPR in the NOMA waveform using SLM algorithm for 64-QAM

In Non-Orthogonal Multiple Access (NOMA) technology, a paradigm shift in wireless communication is observed, as it allows numerous users to simultaneously transmit data over the same spectral resources in a non-orthogonal manner. Despite the advancements it brings to the table, NOMA is faced with a notable technical obstacle, that being the elevated Peak-to-Average Power Ratio (PAPR). Elevated PAPR levels can lead to less efficient use of power and a deterioration in the fidelity of the signal. We proposed a Selective Mapping (SLM) as a powerful solution to address this issue. SLM is a PAPR reduction technique that applies controlled phase rotations to the symbols of different users before transmission, aiming to minimize the PAPR of the combined signal. In the context of NOMA, the application of SLM offers several notable advantages. SLM modifies the Power Spectrum Density (PSD), leading to a more uniform and spectrally efficient power distribution over the frequency spectrum. This enhancement in spectral efficiency concurrently lowers the risk of disrupting nearby frequency channels. Moreover, SLM exerts a favorable influence on the Bit Error Rate (BER) in NOMA systems, which contributes to improved transmission accuracy. Utilizing SLM to achieve lower PAPR values significantly diminishes the probability of symbol errors, thus enhancing the dependability. Additionally, it has been observed that the method in question delivers an improvement in PAPR, BER, and PSD with gains of 2.2 dB, 4 dB, and -620 respectively, in comparison to traditional SLM techniques.

High-speed spatial light modulation enabling 25-Gbit/s twisted light encoding/decoding and 260-m security free-space data transmission.

Spatial domain of light beam is an important degree of freedom to be extensively explored. As a set of spatial domains, twisted lights have some natural properties such as orthogonality and security, providing great potentials in optical communications especially for data encoding/decoding. However, the speed of traditional spatial light modulators has always been criticized. Here we present a hundred-meter security free-space data transmission based on high-speed spatial light modulation by exploiting temporal-to-spatial domain mapping. We demonstrate 25-Gbit/s twisted light encoding/decoding and 260-m security free-space data transmission in the experiment. The encoding/decoding link will lead to 3-dB improvement in bit error rate (BER) performance compared with a single channel in theory and ∼1-dB optical signal-to-noise ratio (OSNR) penalty at the forward error correction (FEC) threshold of 3.8e-3 in practice. The experiment results also show favorable security performance of the proposed encoding/decoding link system.

An efficient ultra-wideband digital transceiver for wireless applications on the field-programmable gate array platform

<p>The ultra-wideband (UWB) technology is a promising short-range communication technology for most wireless applications. The UWB works at higher frequencies and is affected by interferences with the same frequency standards. This manuscript has designed an efficient and low-cost implementation of IEEE 802.15.4a-based UWB-digital transceiver (DTR). The design module contains UWB transmitter (TX), channel, and UWB-receiver (RX) units. Convolutional encoding and modulation units like burst position modulation and binary phase-shift keying modulation are used to construct the UWB-TX. The synchronization and Viterbi decoder units are used to recover the original data bits and are affected by noise in UWB-RX. The UWB-DTR is synthesized using Xilinx ISE<sup>®</sup> environment with Verilog hardware description language (HDL) and implemented on Artix-7 field-programmable gate array (FPGA). The UWB-DTR utilizes less than 2% (slices and look-up table/LUTs), operates at 268 MHz, and consumes 91 mW of total power on FPGA. The transceiver achieves a 6.86 Mbps data rate, which meets the IEEE 802.15.4a standard. The UWB-DTR module obtains the bit error rate (BER) of 2×10<sup>-4</sup> by transmitting 105 data bits. The UWB-DTR module is compared with similar physical layer (PHY) transceivers with improvements in chip area (slices), power, data rate, and BER. </p>

Sparse Neural Network for Detection and Decoding of Non-Binary Polar-Coded SCMA

Sparse code multiple access (SCMA) and polar codes have demonstrated superiority in supporting massive connections and short-packets, which are essential in the uplink transmission of the internet of things. This paper proposes a sparse neural network (SNN) for the detection and decoding of the non-binary polar-coded SCMA. The network consists of the message passing algorithms (MPA) and the sparse belief propagation (SBP) modules, which are immigrated from the MPA and BP algorithms, respectively. The nodes of the bipartite BP factor graph that do not contribute to the belief propagation are pruned to form the SBP module. A structure factor α defined by the ratio of the number of layers in the MPA modules and that of the SBP modules is analyzed to find a balance between the decoding performance and the complexity. The weights are set on the neurons of the layers and trained by the stochastic gradient descent. Finally, the simulation results show substantial improvements in bit error rate with low complexity over the traditional receiver. Specifically, the SNN receiver outperforms the traditional receiver by 2.56 dB in the AWGN channel when the code length is 16, the code rate is 1/2, and the user overloading factor is 150%.

Effect of block size on BER performance of inverse filtering based MIMO FBMC

Filter bank multicarrier (FBMC) is a novel next-generation data transfer technique. FBMC has a large number of benefits over conventional orthogonal frequency division multiplexing (OFDM) techniques. The FBMC scheme suffers from imaginary interference, which degrades the bit error rate (BER) performance at the receiver. In the recent past, inter carrier interference (ICI)-free Alamouti-coded FBMC frame and block repetition schemes for BER improvement were discussed. However, in the analysis, various interfering terms are neglected. In this work to reduce ICI and inter symbol interference (ISI), the concept of inverse filtering is applied while considering other interfering terms. Inverse filtering can be used to reduce the negative impacts of the ICI and ISI. This is determined by mathematical modelling that includes the ICI, ISI, and other interfering parameters. The simulation is performed in MATLABR software. The obtained BER at an SNR of 15 dB is 2.3 × 10−4.The simulation results are also compared with some of the recent methods, and it has been found that the proposed scheme is better in comparison to the considered recent methods.

Comparison of different New Radio (NR) waveforms for wireless communications.

New Radio (NR) waveforms of existing wireless communication systems need further improvement in order to support future wireless communications. NR is the radio interface technology proposed by the 3rd Generation Partnership Project (3GPP) for 5G. Prototype Filter (PF) of NR plays a vital role in performance improvement of wireless systems. NR waveforms can adapt in a better way to different channel conditions. Some of the NR filtering techniques are Filtered-OFDM (F-OFDM), Filter Bank Multi-Carrier (FBMC), and Universal Filtered Multi-Carrier (UFMC). NR waveforms require performance improvement when high reliability, massive connectivity, lower power consumption, and time-critical applications are required. Areas of improvement are Power Spectral Density (PSD), Bit Error Rate (BER), Signal to Interference Ratio (SIR), Doppler Diversity, and Peak to Average Power Ratio (PAPR). This paper compares different performance parameters of Filtered-OFDM, FBMC, and UFMC using existing proto-type filters and novel proto-type filters. The novel and better PFs, described in the paper, were proposed first time by the authors and his research group. Proposed novel prototype filters for FBMC, Filtered-OFDM, and UFMC are respectively Binomial filter and Fractional Powered Binomial Filter (FPBF). With FPBF based OFDM, PSD improvement was 97.5 dB, and BER improvement was 0.07 at 0 dB SNR. With Binomial filter based FBMC, OOBE improvement was 19.7 dB and BER improvement was 0.03 at 0 dB SNR. PAPR improvement with Binomial filter based FBMC was 1.16 dB at 64-QAM and 1.1 dB at 256-QAM. With FPBF based UFMC, improvement of interference level was 122 dB within 3rd~52th sub-bands due to 1st sub-band. BER improvement was 0.09 at 0 dB SNR. SIR improvement was 5. 27 dB with 15 KHz sub-carrier spacing and 16.55 dB with 30 KHz sub-carrier spacing of UFMC. Novel NR filters, discussed in the paper, are good candidates for future 6G wireless systems.

Pilot-based channel estimation in spatial modulated OFDM systems for mobile wireless applications

Spatial modulation Orthogonal Frequency Division Multiplexing (SM OFDM) is a newly developed transmission technique that has been proposed as an alternative for multiple input multiple output (MIMO)-OFDM transmission. Channel estimation is important for coherent data detection in practical scenarios. This paper investigates estimate power, Mean Square Error (MSE) and Bit Error Rate (BER) parameter metrics with channel estimation algorithm for Additive White Gaussian Noise (AWGN) and Rayleigh fading channel on three test environments proposed by International Telecommunication Union (ITU) specified standard model. Simulation output shows that for an AWGN channel, an improvement of approximately 1 dB and 0.3 dB power is obtained with Minimum Mean Square Error (MMSE) estimate and Least Square (LS)-linear/Spline using DFT. Additionally, for various interpolations employed, MSE decreases as Signal to Noise Ratio (SNR) rises, and performance improvement in BER when compared to traditional LS/MMSE estimators also occurs. For Rayleigh fading channel also there arises an enhanced performance in the parameter metrics with MMSE-DFT compared to conventional estimators. In summary, simulation output shows that incorporating DFT algorithm can provide better estimate power, BER and MSE by eliminating the effect of noise externally the extreme channel delay length.

Performance Analysis of 16-FSK Modulator and Demodulator Over Rician Fading Channel by Varying Channel Parameters

This article presents a comprehensive analysis of a 16-FSK modulator and demodulator's performance over a Rician fading channel, which accurately models wireless communication channels with line-of-sight and non-line-of-sight components. The study investigates the impact of various parameters, including the Rician K-factor, Maximum Diffuse Doppler Shift, and Delay Vector, using the MATLAB Simulink communication blockset. The results indicate that increasing the K-factor and delay vector of the channel leads to an improvement in bit error rate. Additionally, it is observed that the maximum diffuse Doppler shift has minimal influence on the bit error rate. These findings provide valuable insights into optimizing the performance of 16-FSK modulation schemes in Rician fading channels, thereby enhancing the design and deployment of wireless communication systems.

Residual Compensation-Based Extreme Learning Machine for MIMO-NOMA Receiver

Extreme learning machine (ELM) uses a simple machine learning (ML) architecture that allows its implementation for smart IoT network operations. However, some inaccuracy lies between the predicted and the actual data during the ELM training, which can be caused due to the limitation of the modeling representation. This paper thus investigates a residual compensation-based ELM (RC-ELM) for its application in designing a receiver for MIMO-NOMA aided IoT systems. In RC-ELM, the base ELM layers determine the relationship between the transmitted and received data and the additional layers of the RC-ELM attempt to compensate the error introduced during the training mechanism. The analysis of the appropriate number of compensation layers for training error minimization is conducted on the basis of the bit error rate (BER) and the error vector magnitude (EVM) performances of the RC-ELM training. A minimum BER improvement of 5% for user 1 and 18% for user 2 is shown with with the aid of RC-ELM for a two-IoT user instance. The EVM is marginally increased by 0.0008% for user 1 and 1.61% for user 2 in training stage. Besides, the RC-ELM receiver is also compared to the minimum mean square error (MMSE), the classic ELM and the trained multilayer perceptron (MLP) receivers in terms of BER and EVM. Both of the ELM receivers show improved performances with respect to the other receivers.

BER Reduction and Capacity Enhancement with Novel Guard Sequence Selection for Multi-Carrier Communication

Orthogonal frequency division multiplexing (OFDM) is an efficient multicarrier scheme that uses different types of guard intervals such as cyclic prefix (CP) and known symbol padding (KSP) (zero padding (ZP), unique word (UW), etc.) in block formation. Among these guard intervals, CP varies for each block, while other guard intervals remain fixed from block to block. These guard intervals efficiently perform channel estimation, synchronization and remove inter-block interference (IBI); nevertheless, none of the existing schemes develop any relationship between the guard interval (sequence) and the data symbols on different subcarriers of the OFDM block. We present a new idea of selecting the guard interval based on the data symbols of a subset of subcarriers in the block and exploit the high auto-correlation of the selected guard sequence to improve the bit error rate (BER) performance of the system. The results based on a fair comparison show that our enhanced orthogonal frequency division multiplexing (eOFDM) scheme inherits significant improvements in BER and the capacity of a multicarrier system as compared to the existing techniques.

Dispersion compensation using cascaded apodized CFBGs under MTDM transmission technique: Enhanced system performance

This study focuses on compensating chromatic dispersion affecting the propagated signal in a standard single mode optical fibers (SSMFs). We propose a model that consists of an identical four stages cascaded linear chirped apodized fiber Bragg gratings (CFBGs) in a post-compensation scheme under Maximum Time Division Multiplexing (MTDM) technique. MTDM is free of the limitations resulting from the fiber nonlinear effects. It, thus, effectively utilizes optical wavelength and operating in various network of different distances and capacities. Therefore, MTDM is designed to be used in the high-capacity and high-speed transmission systems. The evaluation of the proposed model performance is performed through a comparative study using different connections and the most common apodization functions. This comparative study includes three cases based on the used connection scheme. A 10 Gbps Wavelength Division Multiplexing (WDM) 70 km link is simulated under ordinary operating parameters. Optisystem 7.0 is used for the simulation and evaluation process, in terms of two metrics which are quality factor (Q-factor) and bit error rate (BER). In the pre-compensation scheme, the highest results are: Q-factor = 7.664 and BER = 8.99 × 10–15, using the raised cosine apodization function. Using the raised sine apodization in the post-compensation connection realizes the best performance: Q-factor = 8.58 and BER = 4.77 × 10–18. In the symmetrical-compensation scheme and using the raised sine apodization, the proposed model attains the highest Results: Q-factor = 7.49 and BER = 3.53 × 10–14. The proposed model achieves at least 92.6% improvement in BER and 3.75% in Q-factor over the related works. The study is a simulation work and is performed through Optisystem ver. 7.

Low Complexity First: Duration-Centric ISI Mitigation in Molecular Communication via Diffusion

In this letter, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval. Its rationale is to exploit the discarded duration (i.e., the symbol duration outside this optimal interval) to relieve ISI in the target system. Following this idea, we formulate an objective function to quantify the impact of the discarded time on bit error rate (BER) performance. Besides, an optimally reusable interval within the discarded duration is derived in closed form, which applies to both the absorbing and passive receivers. Finally, numerical results validate our analysis and show that for the considered MCvD system, significant BER improvements can be achieved by using the derived reusable duration.

Time-delayed reservoir computing based on a dual-waveband quantum-dot spin polarized vertical cavity surface-emitting laser

In this work, we present numerical results concerning a time-delayed reservoir computing scheme, where its single nonlinear node, is a quantum-dot spin polarized vertical cavity surface-emitting laser (QD s-VCSEL). The proposed photonic neuromorphic scheme exploits the complex temporal dynamics of multiple energy states present in quantum dot materials and uses emission from two discrete wavebands and two polarization states, so as to enhance computational efficiency. The benchmark task used for this architecture, is the equalization of a distorted 25Gbaud PAM-4 signal after 50Km of transmission at 1550 nm. Results confirm that although typical ground-state emitting quantum-dot nodes offer limited performance, due to bandwidth limitations; by exploiting dual emission, we achieved a one-hundred-fold improvement in bit-error rate. This performance boost can pave the way for the infiltration of quantum-dot based devices in high-speed demanding neuromorphic driven applications.