In the present paper, the bit error rate (BER) performance of three detection techniques—minimum mean square error (MMSE), maximum likelihood detection (MLD), and adaptive zero-forcing (ZF) along with a beamforming-based reception scheme in a 4 × 4 multiple-input multiple-output visible light communication (MIMO-VLC) has been investigated. These signal processing techniques have been compared by varying key system parameters such as transmitter separation, receiver separation, field-of-view (FOV), half-power semi-angle (HPSA), operating wavelength, data rate, and photodetector responsivity. Simulation results demonstrate that beamforming consistently outperforms all other schemes at high signal-to-noise ratio (SNR) by 4–8 dB, while MLD results in better performance at low SNR. The result shows that receiver separation is the most critical factor, with wider PD spacing significantly improving BER performance. The FOV exhibit relatively more critical threshold behaviour than HPSA because below a certain threshold value of FOV the system performance completely collapses. Further, it has been found that higher photodetector responsivity, longer operating wavelengths, and lower data rates result in better BER performance using all above-mentioned schemes. The proposed investigation provides valuable design guidelines to implement practical indoor MIMO-VLC systems.

Orthogonal frequency division multiplexing (OFDM), is one of the most commonly used multi-carrier modulation strategy in visible light communication (VLC) systems. The integration of OFDM enhances the immunity against ISI and supports high-speed data to a significant number of users. However, the summation of identical phase symbols during the IFFT process shoots the peak power leading to a high peak-to-average power ratio (PAPR) issue in an OFDM-based optical system (O-OFDM). High PAPR has been a considerable matter of concern over the past decade as it negatively impacts the bit error rate (BER) of the system. The work intends to implement the feed-forward artificial neural network (ANN) in an asymmetrically clipped direct current biased optical OFDM (ADO-OFDM) system in order to mitigate the PAPR while preserving the BER performance of the system. The application of a feed-forward network optimizes parameters in real time to minimize PAPR levels and maintain optimal signal quality in an ADO-OFDM system. The feed-forward ADO-OFDM shows a PAPR of 10 dB for a reference CCDF of 10 −3 achieving a reduction of 2 dB compared to the typical ADO-OFDM system.

Wireless communication systems can enhance their capabilities by exploring new opportunities and addressing emerging challenges through the integration of the Internet of Things (IoT) in 6G networks. Visible Light Communication (VLC) stands out as a promising wireless access technology for IoT devices. This paper presents a novel Teaching-Learning-Based Optimization (TLBO) optimized Intelligent Reflecting Surface (IRS)-assisted VLC system aimed at maximizing Signal-to-Noise Ratio (SNR) and enhancing illuminance uniformity. The proposed system ensures improved communication for IoT devices, particularly in indoor environments. To enhance real-world applicability, the study evaluates system performance under realistic conditions, considering the impact of human and furniture blockages. A detailed investigation is conducted on the achievable SNR performance of the TLBO-optimized IRS-VLC system, demonstrating its ability to maintain robust and reliable communication in obstructed areas. Furthermore, the impact of the proposed system on illuminance uniformity is thoroughly analyzed, showing significant improvements in lighting efficiency and visibility. The effectiveness of the approach is validated using the Coefficient of Variation (CoV), confirming that the system achieves an optimal CoV range of 0-0.2 across the room, ensuring uniform illumination for IoT applications. The SNR and received power improve with increasing IRS elements, with optimal device positioning (e.g., edge-mid) outperforming suboptimal configurations (e.g., corner-opposite) due to geometric reflection efficiency and aperture gain, leading to a widening performance gap at larger IRS scales-highlighting the critical role of strategic IoT device placement in fixed sensor deployments. The findings highlight the potential of TLBO-optimized IRS-VLC systems in overcoming physical obstructions and improving both communication and lighting conditions in smart indoor environments. Additionally, detailed convergence analysis demonstrates that TLBO performs better than Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in terms of convergence speed, higher fitness value and lower sensitivity to initial conditions, making it most suitable for real-time IRS-VLC based IoT applications.

This research introduces a fingerprint-based artificial neural network approach for visible light positioning systems. The study evaluates four different light emitting diode (LED) configurations-square, rectangular, triangular, and circular-within a 5m×5m×3m indoor environment to determine which arrangement delivers the highest positioning accuracy. The analysis employs a receiver moving in a circular path within the receiver plane for the estimation of positioning accuracy across the entire trajectory. Comprehensive simulation results, including the cumulative distribution function, frequency distribution of positioning errors, and error magnitudes at different receiver locations, demonstrate that the mean positioning errors (in centimeters) are 15.2893, 12.4548, 52.5016, and 9.8749 for square, rectangular, triangular, and circular configurations, respectively. The findings indicate that the circular arrangement yields superior performance with minimal positioning error. This configuration creates consistent signal strength gradients across all directions, eliminating potential "dead zones" and maximizing line-of-sight connections between LEDs and the receiver regardless of position. The superior performance of the circular configuration underscores the significant impact of geometric arrangement on positioning accuracy, even when utilizing identical numbers of LEDs and signal processing techniques.

Since the early 1970’s, the world has become increasingly sensitised to environmental issues which cut across many disciplines and occur at different spatial scales. The unprecedented population growth and advanced technology both have led to man’s impact on environment becoming appreciable, so that, there is mounting pressure on both environment and resources. Concerns for environment have been echoed at various international conferences and also have been discussed at inter-governmental levels. In India concern for environment had started in late 1970. To protect environment various constitutional amendments were made in the first place. Environmental legislations were enacted from time to time to protect the environment followed by environmental policies.

Abstract The present paper analyzes the performance of vehicle-to-vehicle (V2V) visible light communication (VLC) systems across various practical scenarios. Metrics including bit error rate (BER), frequency response, signal-to-noise ratio (SNR), channel capacity, and time delay are analyzed for three distinct scenarios. Results indicate that inadequate lateral displacement between vehicles of adjacent lanes can significantly impact BER performance. Moreover, frequency response analysis reveals undesirable periodic variations in scenario-2, posing challenges for V2V communication. In the case of non-line-of-sight (NLoS) communication (scenario-3), both longitudinal and lateral displacements exhibit notable impacts across all analyzed metrics.

A comparative analysis of 3D positioning error for two different configurations using different layouts of visible light communication (VLC) systems is presented in this paper. The Received Signal Strength (RSS) has been implemented for indoor localization systems using Line-of-Sight (LoS) and diffused reflection signals. The room size for configuration-1 is 5 m x 5 m x 3 m, and the distance between adjacent LEDs is 2.5 m, 2.00 m, and 1.5 m for cases-1, case-2, and case-3, respectively, whereas the room size for configuration-2 is 7 m x 7 m x 5 m, and the separation between the LEDs is 3.5 m, 3 m, and 2.5 m for their respective cases. Through investigation, it has been shown that when only LS signal is considered, the separation between LEDs may not be an issue because positioning error changes by a very small amount as the separation between LEDs changes. The results show that as the distance between adjacent LEDs decreases, the received signal strength for LoS and L-R1 signals increases. However, positioning error and BER rise, while the bit rate falls. Furthermore, the positioning error Vs receiver plane height for all three cases in configuration-1 is the same up to a height of 2.89 m, whereas the positioning error in configuration-2 is the same up to 4.4 m for all cases. The positioning error for case-1 decreases as the height in configuration-1 exceeds 2.89 m. Similarly, after reaching a height of 4.4 m for case-2, the positioning error in configuration-2 decreases. The LoS positioning error versus semi angle phi 1/2$$ {\varphi}_{1/2} $$ of the LED as well as the FOV of the receiver has been simulated for different positions of the receiver in configuration-1. The investigation shows that the minimum positioning error is achieved at phi 1/2$$ {\varphi}_{1/2} $$ and FOV equal to 66.660 for all the positions of the receiver in the room. Thus, before configuring a practical indoor VLC geometrical model, proper VLC configurations such as LED separation, FOV of the receiver, semi angle of LED, and receiver height should be chosen based on the room dimensions. This paper presents a comparative analysis of 3D positioning error in two VLC system configurations using RSS for indoor localization. Findings indicate that positioning error is minimally affected by LED separation with only LoS signals. Reduced LED distances enhance signal strength but increase positioning error and BER while lowering the bit rate. Optimal configurations, including LED separation, receiver FOV, semi-angle, and height, are critical for minimizing positioning error based on room dimensions. image

This study investigates the Bit Error Rate (BER) performance of a 4×4 MIMO visible light communication (VLC) system using several detection techniques, including Minimum Mean Square Error (MMSE), Maximum Likelihood Detection (MLD), Adaptive Zero-Forcing (ZF), and Beamforming. The evaluation is conducted across multiple scenarios involving variations in transmitter separation (dtr), receiver separation (drx), field-of-view (FOV), and half-power semi-angle (HPSA). Results reveal that beamforming consistently outperforms other detection methods at higher SNR levels, while at lower SNR levels, MLD yields the lowest BER, followed by Adaptive ZF and MMSE. Optimal BER performance occurs with a receiver separation of 40 cm when LED separation is fixed at 2.5 m. With fixed PD separation, the BER is minimized at a 2 m separation between LEDs. For different FOV settings, each detection method achieves optimal BER at specific FOV angles: pseudo-inverse, MMSE, and Adaptive ZF perform best at 60 degrees, while MLD and beamforming show improved BER at 50 degrees. Additionally, varying the HPSA shows that all detection methods perform best at an HPSA of 40 degrees.

The current study explores the performance of an indoor Visible Light Communication (VLC) system using both linear and nonlinear regression methodologies. The analysis is conducted within the dimensions of a 5 m × 5 m × 3 m room, examining the channel frequency response, including magnitude and phase responses. Predictions are made using both linear and nonlinear regression models at distinct locations—center, side, and corner positions. Results indicate that the linear regression model outperforms the nonlinear approach, providing more accurate predictions of the true response. This conclusion is further supported by simulations of the bit error rate (BER) versus SNR for a 4 × 4 Multiple-Input Multiple-Output (MIMO) VLC system, employing both linear and nonlinear regression methods.

This study compares the performance of three fingerprint-based models—K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Deep Neural Network (DNN)—for indoor Visible Light Positioning (VLP) systems. The simulation aims to determine the most accurate and reliable method for precise indoor positioning applications. The results indicate that the Fingerprint-ANN model outperforms both the Fingerprint-KNN and Fingerprint-DNN models in terms of positioning accuracy. With a maximum error of 0.0136 m, a minimum error of 1.5449 × 10⁻⁵ m, and an average error of 0.0016 m, the Fingerprint-ANN model achieved significantly lower errors compared to the Fingerprint-KNN (maximum error: 0.1803 m, minimum error: 0 m, average error: 0.0548 m) and Fingerprint-DNN (maximum error: 1.1106 m, minimum error: 1.6236 × 10⁻⁵ m, average error: 0.6067 m) models. The superior performance of the Fingerprint-ANN model can be attributed to its ability to capture complex non-linear relationships within the signal strength data, leading to higher accuracy and reliability. Furthermore, the resemblance of the positioning error mesh plot of the Fingerprint-DNN model to that of the trilateration method suggests that while DNNs can model complex relationships similar to traditional methods, they might require more data or tuning to reduce error further. Overall, these findings highlight the potential of the Fingerprint-ANN model for achieving enhanced precision in indoor VLP systems, making it the preferred method for applications requiring high accuracy and reliability.

Due to its many advantages, orthogonal frequency division multiplexing (OFDM) is frequently utilised in optical wireless systems to achieve rapid data transmission. Hybrid asymmetrically clipped optical OFDM (HACO-OFDM) is a widely utilised approach in OFDM-based visible light communication (VLC) systems because it allows for high-rate burst transmission while maintaining reliability in a multi-path fading environment. Meanwhile, the congenital nonlinear characteristic of LEDs is a critical hindrance in VLC OFDM systems due to the high peak-to-average power ratio (PAPR) of OFDM signals. The high PAPR necessitates a large input back-off power to operate LEDs in the linear zone, lowering the BER of OFDM-based optical devices. In order to reduce PAPR and ameliorate non-linearity in the HACO-OFDM system, this work offers a hybrid precoder with -law compander approach. The auto-correlation association of modulated data symbols is suppressed by pre-processing in the frequency domain using a preset precoder matrix (PM), resulting in a low PAPR score. PAPR is further reduced by compressing the precoded signal in the time domain with the -law compressor. The PAPR of the proposed design is just 5.1 dB. It also reduces the non-uniform SNR spread among data symbols and mandates a low SNR for a 10 ^-5 reference bit error rate (BER). Furthermore, with a reference BER of 10 ^-3 , the hybrid approach reduces the optical bit energy required for high-speed data transmission. The metric used to calculate the system’s non-linearity is the percentage error vector magnitude (EVM

The paper presents the longitudinal and lateral stability analysis of an aerostat tethered in a steady wind. The parametric trend study showing the effect of variation of different parameters on longitudinal stability boundaries of an aerostat has also been presented. In contrast with the conventional airplane, the equations of motion for the tethered aerostat included buoyancy forces, apparent mass terms and static forces resulting from the tether cable. The analysis consisted of mathematical modeling and its use to compute the stability characteristics for the longitudinal and lateral cases followed by the parametric trend study carried out by varying the dimensional, aerodynamic and other parameters of the aerostat for the longitudinal case. Graphical results show that the aerostat is stable for longitudinal as well as lateral case. The parametric trend study presented, thereafter, suggested that the judicious and feasible choice of various aerostat parameters could be utilized to design a new aerostat that can remain stable for wide range of wind velocities.

The fifth-generation (5G) mobile communication technology is now being widely deployed in a number of nations, along with a significant rise in the number of 5G customers. However, it is becoming more and more clear that it is time for both academics and business to shift their emphasis towards the next generation as the mobile telecommunications market continues to change. At this point, it is crucial to provide a thorough review of the state of the art and look forward to the future of communications. This article tries to achieve this goal by providing a thorough analysis designed to outline the sixth generation (6G) system's characteristics. The motivations, use cases, use scenarios, needs, key performance indicators (KPIs), architecture, and enabling technologies that will characterize 6G are all covered in this investigation.

In the present paper, the impact of transceiver configuration has been analyzed for the positioning error in indoor visible light communication system using the received signal strength (RSS) technique. The analysis has been done with four Light Emitting Diodes (LEDs) through Line-of-Sight (LoS) as well as LoS plus first reflection (L-R1) signals. Three different cases have been considered with adjacent separation between each LEDs as 2.5m, 2.00m and 1.5m. The result shows that when the separation between adjacent LEDs is decreased then average received signal strength of LoS as well as L-R1 signals increase but it also causes average positioning error to increase.

The current paper proposes a hybrid OMA-Co-operative-NOMA (HOCN) scheme that unifies visible light communication and radio frequency (RF) systems by utilising the perfectly synchronised Wireless Information and Power Transfer (SWIPT) technique. The NOMA employs successive interference cancellation, a computationally demanding task that shortens battery life. As a result, in a cooperative system, an energy harvesting scheme SWIPT is also implemented to ensure that near users have enough power to relay data to far users. The achievable rate of users is the metric used to quantify the performance in this work. The proposed HOCN system's performance has been compared to that of the SC-NOMA and TDMA systems using a range of user pairings, including near-far, near-near, and far-far (N-F, N-N, F-F). Furthermore, the impact of various field of view (FOV - 40 0 & 60 0 ) combinations of near and distant user with varying LED semi-angles has been investigated. Using different LED transmit powers, the impact of power harvesting efficiency on the far user rate as well as the sum rate has been examined. The results show that proposed HOCN system is better option as it requires less transmitted power as well as smaller optical attocell.

Visible light communication (VLC) is becoming more popular as spectrum scarcity becomes a serious issue for wireless communication. Availability to the base network, on the other hand, will probably alter the system for communication, incurring significant costs. Power line communication (PLC) technology appears to be an excellent fit for VLC, providing electricity and connecting VLC to the backbone system. In the present paper a hybrid PLC- VLC has been experimentally demonstrated with very less budget as well as low complexity. For PLC and VLC channel, Frequency Shift Keying (FSK) and On-Off Keying (OOK) modulation technique has been implemented. In the VLC system, 4× 1 MISO system has been employed. A pulse signal has been generated from the MATLAB Simulink and this signal has been transmitted through PLCMISO-VLC channel. All the waveforms of the signal have been traced on Digital Oscilloscope (DSO) successfully.

A deep neural network-based channel estimation (CE) for multiple input multiple output (MIMO) DC-biased Orthogonal Frequency Division Multiplexing (DCO-OFDM) in an indoor visible light communication (VLC) system is presented in this paper. A complex-valued neural network (CVNN), a deep neural network (DNN) derivative that can directly use complex numbers as opposed to generic deep learning technologies that use real numbers, was used to estimate channels. Optimizers such as adaptive moment estimation (Adam) and stochastic gradient descent (SGD) are used to adapt CVNN's attributes and aid in loss reduction. The proposed CVNN-based CE outperformed the traditional Least Square (LS) estimator in terms of BER performance of LOS, LOS with first-reflection (L-R1), and LOS with first-reflection and second-reflection (L-R1-R2). The results show that the suggested CVNN-based CE may be utilized in OFDM systems without a clue of channel information for both LOS and non-LOS cases.

The power domain non-orthogonal multiple access (NOMA) has been considered as an outstanding candidate for 5G visible light communication networks. Due to the various salient features like robustness to multi-path fading, low latency, high compatibility with multiple input multiple output systems, etc. orthogonal frequency division multiplexing access (OFDM) is foreseen to exist in 5G systems. NOMA-based optical OFDM (O-OFDM) is expected to be a promising candidate for supporting high rate internet connectivity, especially for green indoor mobile networks. The OFDM waveform suffers from high PAPR issue. The high PAPR degrades the BER and also enhances the non-linearity in an O-OFDM system. In this paper, a hybrid technique using a blend of precoder with compander is implemented to reduce the PAPR of NOMA based DC-biased O-OFDM (DCOOFDM) system. The proposed NOMA DCO-OFDM system exhibits a low PAPR of only 4.3 dB. Also, for a reference bit error rate of 10(-4), the proposed technique displays an error vector magnitude of 13.2 dB.

The power domain non-orthogonal multiple access (NOMA) protocol is proposed as a viable 5G visible light network solution. Orthogonal frequency division multiplexing access (OFDM) is anticipated to exist in 5G systems due to a number of important characteristics, including multi-path fading tolerance, low latency, and high compatibility with multiple input multiple output (MIMO) systems. The optical OFDM (OOFDM) system based on NOMA is a promising contender for supporting fast packet switching network especially in green indoor mobile networks. The OFDM waveform suffers from high PAPR issues. The high PAPR degrades the BER and also enhances the nonlinearity in an O-OFDM system. This research uses a hybrid technique that combines a precoder and a $\mu$-compander to reduce the PAPR of a NOMA-based DC-biased O-OFDM (DCO-OFDM) system. The proposed NOMA DCOOFDM system exhibits a low PAPR of only 4.3 dB. Also, for a reference bit error rate of 10-4, the proposed technique displays an error vector magnitude (EVM) of 13.2 dB.