The research team, led by Hoa T. Le of the University of Transport and Communications in Vietnam together with collaborators including Peter LoPresti of the University of Tulsa, built a complete FSO-SLIPT testbed in which atmospheric turbulence was deliberately generated, measured, and varied as an experimental parameter. This distinguishes the work from earlier laboratory demonstrations of optical data-and-power transfer, which typically ran over short, still-air paths where turbulence was never controlled or characterized. In those prior experiments, such as demonstrations of gigabit-per-second data-and-power links using gallium arsenide photovoltaic cells, the focus fell on receiver electronics and modulation formats rather than on the physics of a fluctuating channel. The new study instead treats turbulence as the primary variable, bringing experimental practice into line with the statistical channel models that theorists have relied on for years.

The underlying system architecture is elegant in its simplicity. At the transmitter, an on-off keying generator produces an alternating-current signal carrying the information, while a separate fiber-coupled laser at 1550 nanometers supplies a direct-current bias that represents pure optical energy. A wideband coupler merges the two into a single composite beam, so the light leaving the transmitter is simultaneously a data stream and a power beam. At the receiver, an indium gallium arsenide photodetector converts the incoming light back into an electrical signal, which is then fed into an AC-DC separator circuit. A high-pass branch, built from a 0.47-microfarad capacitor and a 560-ohm resistor, routes the rapidly varying data component to the decoder. A low-pass branch, using a 10-picofarad capacitor and a 120-kilo-ohm resistor, extracts the slow direct-current component that charges the energy-harvesting circuit. This AC/DC separation scheme is the simplest of the three main SLIPT receiver architectures, which also include time-splitting and power-splitting designs, and it allowed the team to measure both communication quality and harvested power from the same received signal.

The turbulence itself was created inside a customized chamber 77 centimeters long, fitted with two heaters along its base and two fans on its sides. By heating the air and stirring it with fans, the researchers produced convective pockets of air with different temperatures and densities, mimicking the refractive-index fluctuations that plague real outdoor and airborne optical links. Two scenarios were tested. Weak turbulence was generated with the chamber at 22 degrees Celsius and fans at maximum speed, yielding a scintillation index of 0.007 and a refractive-index structure constant of 5.41 times ten to the minus eleven. Moderate turbulence, produced at 100 degrees Celsius with heaters and fans both at full power, raised the scintillation index to 0.096 and the structure constant to 7.32 times ten to the minus ten. Both values fall in the weak-to-moderate regime, where the received intensity is well described by a log-normal statistical distribution, a fact the team confirmed by fitting the measured voltage histograms with log-normal probability density functions that achieved root-mean-square errors of just 0.016 and 0.013 respectively.

On the communication side, the results followed the expected and sobering pattern. The team transmitted on-off-keyed data at bit rates from 0.5 to 4 megabits per second and estimated the bit-error rate from the Q-factor, a standard quality metric derived from the mean and standard deviation of the voltages representing digital ones and zeros. At every bit rate, moderate turbulence produced a consistently higher bit-error rate than weak turbulence. The mechanism is visible in the voltage statistics: under weak turbulence the probability distributions for ones and zeros remain narrow and well separated, preserving a wide decision margin at the receiver. Under moderate turbulence, scintillation broadens both distributions and pulls them into overlap, shrinking the margin between the decision thresholds and driving errors upward. Theoretical BER curves computed from the measured voltage statistics tracked the experimental curves closely, with the small residual gap attributed to circuit noise and non-idealities in the AC-DC separator that the simplified linear model does not capture.

The energy results told a very different story. Average harvested power, calculated from the root-mean-square voltage on the DC branch across a one-megaohm load, showed no clear dependence on bit rate, fluctuating in both turbulence scenarios. But when the researchers compared turbulence strengths, the moderate-turbulence case consistently delivered more harvested power than the weak-turbulence case. Far from being a measurement artifact, this counterintuitive outcome follows directly from the mathematics of fluctuating signals. The harvested-power estimate depends on the second moment of the received intensity, and the scintillation index is defined as the normalized second moment. For a fixed mean received power, increasing the scintillation index mathematically increases the second moment, raising the RMS voltage and therefore the estimated harvested power. The effect requires no exotic nonlinear harvesting circuitry; it emerges purely from second-order signal statistics.

The voltage distributions on the DC branch make the physics tangible. Under weak turbulence, the DC voltage forms a sharp, narrow peak, indicating a steady and predictable power level. Under moderate turbulence, the distribution spreads dramatically, and occasional voltage samples climb to notably higher values. Those intermittent bursts of strong received intensity inflate the average harvested power, even though they also make the instantaneous power less reliable, as reflected in the higher power variance measured in the moderate case, 9.998 times ten to the minus eleven compared with 8.371 times ten to the minus eleven under weak turbulence. In other words, turbulence trades stability for intensity: the receiver sees a rockier energy supply, but on average a richer one.

The experimental protocol was designed for statistical rigor despite the modest scale of the benchtop link. Data were recorded at 10 million samples per second across 200 trials per measurement, totaling one second of capture, which corresponds to roughly one million bits at the 1-megabit-per-second setting and scales proportionally at higher rates. All measurements were conducted in total darkness to eliminate ambient light, and the scintillation metrics were computed from signals measured after the photodetector but before the separation circuit, with noise removed. The authors are candid about one methodological limitation: the Q-factor approach to BER estimation assumes approximately Gaussian decision statistics, whereas log-normal intensity fluctuations make the AC voltage slightly non-Gaussian. They argue that the method remains a widely accepted practical estimator when large sample counts are available, and the close agreement between experimental and theoretical BER trends supports that claim, while direct bit-by-bit error counting is deferred to future work.

The broader significance of the study lies in what it implies for the design of future optical wireless networks. Sixth-generation systems envision connectivity not only across massive terrestrial and underwater sensor fleets but also for unmanned aerial vehicles, high-altitude platforms, and satellites, all competing for scarce radio-frequency spectrum. Free-space optics offers fiber-like data rates and immunity to RF interference, and SLIPT extends it into a genuine data-plus-power delivery system for energy-constrained nodes. Until now, engineers designing such systems had only simulation results to guide them, and those simulations uniformly treated turbulence as a pure impairment. The new experimental evidence shows that this framing is incomplete. A system optimized purely for communication reliability would suppress turbulence effects wherever possible, yet the same fluctuations that corrupt the data can be exploited to boost energy delivery. The authors conclude that SLIPT-enabled FSO systems demand joint optimization of communication and energy objectives rather than conventional communication-centric design, and they point toward adaptive transmission strategies that dynamically balance reliability against harvested power as channel conditions change. While the 77-centimeter laboratory path is far shorter than any real deployment, the team notes that scintillation statistics over longer links obey the same underlying physics, so the observed trade-off is expected to persist. If the finding holds at scale, the turbulent air that once seemed destined to ruin laser links may instead become a resource to be managed, turning an old adversary of free-space optics into an unexpected ally for the power-hungry devices of the 6G era.

Subject of Research: Experimental evaluation of simultaneous lightwave information and power transfer over turbulent free-space optical links

Article Title: Laboratory evaluation of simultaneous lightwave information and power transfer in turbulent FSO environments

Article References: Le, H. T., Seifi, E., Nguyen, T. V., Pham, H. T., Dang, N. T., & LoPresti, P. (2026). Laboratory evaluation of simultaneous lightwave information and power transfer in turbulent FSO environments. Results in Optics, Article 101182. https://doi.org/10.1016/j.rio.2026.101182

Image Credits: AI Generated

DOI: 10.1016/j.rio.2026.101182

Keywords: free-space optics, SLIPT, atmospheric turbulence, wireless power transfer, 6G networks, bit-error rate, scintillation, energy harvesting, optical communications, log-normal fading, photodetector, laboratory experiment

The team, led by Abhijit Patil of K J Somaiya Institute of Technology in Mumbai, together with Ramesh Shelke of S. S. Jondhale College of Engineering and Dilendra Hiran of Pacific Academy of Higher Education and Research University, set out to fix a specific weakness in earlier watermarking research. Many existing schemes process audio in overlapping segments when embedding a watermark, which drives up computational cost without necessarily improving security. Overlapping windows are a standard tool in signal processing because they smooth transitions between frames, but in the watermarking context the authors argue that the redundancy they introduce is largely wasted effort. Their system instead guarantees non-overlapping segmentation from the very first stage, trimming the computational overhead that has plagued previous approaches.

The pipeline begins with a technique borrowed from classical spectral analysis: the FlatTop window. Windowing functions are mathematical tapers applied to short stretches of a signal before further processing, and each shape trades off different properties. FlatTop windows are prized for their exceptionally flat passband, which means they measure the amplitude of frequency components with minimal distortion, at the cost of a wider main lobe. In RAWS-SBS, the FlatTop window slices an incoming audio signal into frames that do not overlap, establishing a clean, computationally frugal foundation for everything that follows. This choice matters because every subsequent operation, from transform to embedding to extraction, operates on these frames, so inefficiency at the segmentation stage multiplies through the whole system.

Once segmented, the frames undergo a decomposition step using what the authors term a Polar Cartesian-based Discrete Wavelet Transform, or PC-DWT. The discrete wavelet transform is a workhorse of modern signal processing, breaking a signal into components that capture both frequency content and where in time that content occurs, unlike the plain Fourier transform, which discards timing information. The polar Cartesian variant reformulates this decomposition, and the transformed coefficients are then arranged into matrices from which features are extracted. These features feed the next critical decision: where, exactly, inside the audio should the watermark bits live? Placing a watermark carelessly makes it audible to listeners or fragile under compression and filtering; placing it too timidly makes it easy to destroy.

To answer that placement question, the researchers developed an optimization method they call Gamma Functional Energy Valley Optimization, or GFEVO. Optimization algorithms of this family search a large space of candidate solutions for the one that best balances competing objectives, and here the objective is finding embedding locations that maximize robustness while preserving perceptual quality. The reported results suggest the search paid off: the GFEVO stage achieved a Perceptual Evaluation of Speech Quality score of 4.256, a metric on a scale where values near 4.5 indicate quality nearly indistinguishable from the original. In practical terms, a listener hearing the watermarked audio would be very hard pressed to notice that anything hidden inside it at all.

Security enters the pipeline through the most colorful component of the system: Blancmange Curve Cryptography. The Blancmange curve, also known as the Takagi curve, is a fractal defined as an infinite sum of sawtooth-like functions. It is famous in mathematics for being continuous everywhere yet differentiable nowhere, a jagged, self-similar shape that never smooths out no matter how closely you zoom in. The authors harness this fractal’s irregular, deterministic complexity to encrypt the watermark image before embedding, converting the encrypted bits into a vector bit stream. An attacker who extracts the embedded payload without the key would face data scrambled according to the curve’s fractal arithmetic, adding a cryptographic layer on top of the steganographic one. It is a striking example of pure mathematics, a curve named after a wobbly pudding, finding duty in applied security engineering.

Embedding, however, is only half the problem. A watermark is useless if the legitimate owner cannot reliably pull it back out of an audio file that may have been compressed, filtered, resampled, or otherwise degraded on its journey through the digital world. For extraction, the team deployed a deep convolutional neural network with two distinctive design elements. The first is a spatial-and-cross architecture, which lets the network correlate information across different spatial regions of the transformed audio representation rather than treating each location in isolation. The second is a Scaled Gaussian Error Linear Unit, or SGELU, an activation function that governs how signals pass between network layers. Activation functions shape what a neural network can learn, and the Gaussian-error variant blends a smooth probabilistic weighting into the familiar rectified linear unit, potentially giving the extractor a richer response to the subtle statistical traces a watermark leaves behind.

The experimental numbers are where the system makes its case. The proposed method achieved a minimum Bit Error Rate of 0.03232, meaning that when the hidden watermark was extracted, fewer than four bits in every hundred came back wrong, a low error rate that translates directly into more reliable ownership verification. On overall watermarking accuracy, the system reached 97 percent. The authors benchmarked this against several established deep learning baselines, reporting improvements of 3.2 percent over a standard DCNN, 4.3 percent over a conventional CNN, 7.8 percent over a deep belief network, and 11.5 percent over a plain deep neural network. Those margins may look incremental, but in a field where a few percentage points of accuracy can determine whether a copyright claim holds up, they are meaningful.

Context helps explain why this line of research is heating up. Audio watermarking has moved far beyond its original role of tagging music files for royalty tracking. Recent work in the field targets desynchronization-resilient schemes that survive time-stretching attacks, blind quantum watermarking, neural approaches with invertible dual embedding, and systems designed specifically to detect deepfake audio proactively. As generative models make synthetic speech and music indistinguishable from human-created recordings, the ability to embed a verifiable, tamper-evident signature inside audio is becoming a frontline defense. Watermarks that survive aggressive processing while remaining inaudible are the technical backbone for proving that a clip is authentic, or flagging one that is not.

The RAWS-SBS study, received by the journal in 2023 and published on 31 August 2026 after an unusually long revision cycle, reflects that broader urgency. Its authors report no funding source and no competing interests, and they state that the data generated in the study are available from the corresponding author on reasonable request. Whether fractal-based encryption and non-overlapping segmentation become standard ingredients of future watermarking systems remains to be seen, but the work illustrates a clear trend: the fight to authenticate digital audio is increasingly fought with hybrid tools, marrying signal processing classics like wavelet transforms and windowing functions to deep networks and, now, to the jagged elegance of nowhere-differentiable fractals. For an industry drowning in unattributable sound, every percentage point of extraction accuracy counts.

Subject of Research: A robust deep learning-based audio watermarking system using Blancmange curve cryptography for digital media security

Article Title: A robust audio watermarking system with SC-SGELU-DCNN and BCC algorithm for security

Article References: Patil, A., Shelke, R., & Hiran, D. (2026). A robust audio watermarking system with SC-SGELU-DCNN and BCC algorithm for security. Multimedia Tools and Applications, 85(9), Article 725. https://doi.org/10.1007/s11042-026-21865-8

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21865-8

Keywords: audio watermarking, deep learning, convolutional neural network, Blancmange curve cryptography, discrete wavelet transform, bit error rate, digital security, copyright protection, signal processing, SGELU activation, optimization, multimedia forensics

The challenge stems from the very technique that makes optical wireless so spectrally efficient: high-order quadrature amplitude modulation, or QAM. Schemes such as 256-QAM and 512-QAM pack enormous amounts of data into each symbol by using extremely dense constellations of amplitude and phase combinations. But as the constellation density grows, the decision boundaries between neighboring symbols shrink to razor-thin margins, making the signal exquisitely sensitive to noise, phase fluctuations, and the nonlinear distortions introduced by optical components such as light-emitting diodes and laser diodes. In intensity-modulated direct-detection systems, where light intensity cannot go negative, engineers must also contend with a high peak-to-average power ratio, which further degrades signal quality. Traditional receivers that rely on analytical models and linear approximations simply cannot capture these nonlinearities, and their performance collapses precisely where 6G needs it most.

The research team built their evaluation around a two-user optical NOMA system, in which both users share the same time and frequency resources through power-domain superposition coding. The user with the weaker channel is allocated 80 percent of the transmit power while the stronger user receives 20 percent, and at the receiver a successive interference cancellation procedure separates the overlapping signals. The channel model incorporated a realistic battery of impairments: distance-dependent path loss with an exponent of 2.2, Rayleigh fading, additive white Gaussian noise, optical nonlinear distortion from the light source, and hardware impairments including synchronization errors. This deliberately harsh environment was designed to emulate the conditions a real 6G optical link would face, with independent channel realizations generated for each user to reflect heterogeneous propagation conditions.

Against this backdrop, the authors evaluated six detectors within a single unified framework. The minimum mean square error (MMSE) detector offers a favorable complexity-performance trade-off but remains fundamentally linear. The QR-decomposition with M-algorithm maximum likelihood detector (QRM-MLD) approaches optimal performance through tree search but becomes computationally prohibitive at high modulation orders. Convolutional neural network (CNN) detectors learn spatial signal patterns efficiently but cannot model temporal dependencies, while recurrent neural network (RNN) detectors capture channel memory and time correlations at the cost of slower convergence. Two hybrids, CNN-MMSE and the newly proposed RNN-MMSE, combine statistical estimation with deep learning refinement. Prior studies had examined these techniques only in isolation; the unified comparison is itself a significant contribution.

The proposed RNN-MMSE detector operates in three stages. First, an RNN learns the channel characteristics directly from pilot symbols, replacing conventional pilot-based estimation and capturing temporal variations in the process. Second, the MMSE stage applies a statistically optimal linear estimate that suppresses noise and linear interference, producing a structured initial symbol estimate. Third, a second RNN refines that estimate by learning only the residual nonlinear distortions and temporal dependencies that the linear stage could not remove. The authors provide a theoretical justification rooted in the orthogonality principle of linear estimation: because the MMSE output extracts all linearly available information, the residual presented to the recurrent network has a substantially smaller variance than the raw signal, which smooths the optimization landscape, lowers gradient variance, and accelerates training convergence.

The simulation results are striking. At 64-QAM, conventional optical NOMA detection required approximately 13.5 dB of signal-to-noise ratio to achieve a bit error rate of 10 to the minus 3, while the proposed RNN-MMSE detector reached the same target at just 6.5 to 7 dB. As the modulation order climbed, the gap widened. At 128-QAM the hybrid detector delivered a 7 to 8 dB improvement, at 256-QAM an 8 to 9 dB gain, and at 512-QAM, the most demanding scheme tested, it achieved a 9 to 10 dB advantage, reaching the target error rate at roughly 12.5 to 13 dB where the conventional baseline needed 21 to 22 dB. Standalone CNN and RNN detectors and the CNN-MMSE hybrid improved on the classical methods but consistently fell short of the proposed architecture.

Spectral behavior told a similar story. Using Welch power spectral density estimation with Hamming windowing, the team measured out-of-band leakage across all detectors. Conventional optical NOMA exhibited in-band power spectral density around minus 40 dB/Hz with poor suppression near minus 60 dB/Hz, while the RNN-MMSE detector pushed spectral leakage below minus 130 dB/Hz at 512-QAM, indicating excellent sidelobe suppression and minimal adjacent-channel interference. Training convergence favored the hybrid as well: over 20 epochs the RNN-MMSE model began at roughly 85 percent accuracy and converged to about 90 percent, outperforming standalone models by 18 to 32 percentage points. Statistical validation across 20 independent Monte Carlo runs confirmed the results were reproducible, with a standard deviation of only 0.22 dB on the key SNR metric and a 95 percent confidence interval of plus or minus 0.10 dB.

Practicality was a central concern. The entire framework was implemented in MATLAB R2024a on a workstation with an Intel Core i7 processor, 32 GB of RAM, and an NVIDIA RTX 3060 GPU. Training on 100,000 generated signal samples took only 14 to 16 minutes, and inference required approximately 3.8 milliseconds per OFDM frame, fast enough for near-real-time deployment. Memory consumption stayed below 800 MB during inference. The authors acknowledge that the hybrid architecture carries more computational overhead than a standalone linear detector, with complexity of the order of N cubed for the MMSE stage plus N squared terms for the recurrent stage, but they argue the performance gains justify the cost, particularly since the heavy training can be performed offline once while deployment requires only lightweight forward inference that GPUs, FPGAs, or edge accelerators can handle.

The study is candid about its limits. All results derive from Monte Carlo simulation rather than a physical testbed, and the authors identify experimental validation on an LED- or laser-based intensity-modulation link with software-defined-radio baseband processing as an immediate priority. Their comparison with Transformer-based detectors draws on literature benchmarks obtained under different channel models rather than a controlled head-to-head experiment, and their robustness analysis of imperfect channel estimation, imperfect interference cancellation, and synchronization errors is mechanistic rather than simulated. The two-user configuration, while modular and scalable in principle, will need extension to denser multi-user networks with adaptive grouping and dynamic power allocation. Even so, the findings make a compelling case that hybrid intelligent detection is not an incremental refinement but a necessary evolution for 6G optical networks, and the roadmap toward testbed validation, attention-based comparisons, and large-scale multi-user trials is already clearly drawn.

Subject of Research: Neural network-based signal detection for 6G optical non-orthogonal multiple access systems with high-order QAM modulation

Article Title: Comprehensive analysis of 6G optical NOMA waveforms using neural network-based detection for high-order QAM modulation

Article References: Kumar, A., Revathi, V., Gaur, N., & Nanthaamornphong, A. (2026). Comprehensive analysis of 6G optical NOMA waveforms using neural network-based detection for high-order QAM modulation. Results in Optics, 25, Article 101159. https://doi.org/10.1016/j.rio.2026.101159

Image Credits: AI Generated

DOI: 10.1016/j.rio.2026.101159

Keywords: 6G, optical wireless communication, NOMA, QAM, recurrent neural network, MMSE detection, deep learning, bit error rate, power spectral density, IM/DD, OFDM, signal detection