The dataset, published as a FAIR²-certified data article in Frontiers in Communications and Networks, grew out of a point-to-point SWIPT testbed built around multisine waveforms. In a multisine signal, the transmitter does not encode information in the conventional way, by modulating a single carrier with amplitude or phase changes. Instead, the message is carried in the number of sine-wave tones that make up the signal itself. The Cyprus team used tone counts of 2, 4, 8, 16, and 32 as five discrete signal classes, so that a receiver able to infer how many tones are present can recover the transmitted symbol while simultaneously harvesting the energy that the waveform carries.

The hardware behind the measurements reflects the kind of low-cost, low-complexity architecture that SWIPT researchers care about. On the transmit side, an NI USRP 2920 software-defined radio, driven by LabVIEW on an ordinary laptop, generated the multisine waveforms at 915 MHz within a total bandwidth of 200 kHz. A DA-915-01 dipole antenna with 1.7 dBi of gain in the GSM-900 band radiated the signal, deliberately chosen for its simplicity and fixed properties so that no adaptive beamforming degrees of freedom would confound the results. Every waveform was normalized to its amplitude envelope before transmission to prevent digital-to-analog converter saturation and front-end nonlinearity, keeping the signals inside the usable dynamic range of the radio.

The receiving chain is where the dataset becomes unusual. Rather than a conventional demodulator, the receiver uses a Powercast P21XXCSR RF energy harvesting module, a diode-based rectifying antenna or rectenna that converts incident radio waves into direct current. The harvested output feeds a WSN-EVAL-01 wireless sensor board and a Microchip XLP PIC24F access point, and the resulting power readout, expressed in milliwatts, is logged in real time through a HyperTerminal interface. Crucially, the same received signal that excites the harvesting circuit also produces the measurement feature, meaning the entire receiver performs its inference on energy-domain observations alone. That makes the data especially relevant to passive or nearly passive IoT receivers that have no budget for conventional signal processing.

The measurement campaign was systematic to a fault. The researchers swept a fully crossed experimental matrix of four transmitter gain settings, 65, 75, 90, and 100 percent of the USRP gain, corresponding to 20.15, 23.25, 27.9, and 31 dB, and six transmitter-receiver separations from 10 to 35 centimeters in 5-centimeter steps. At every one of the twelve resulting gain-distance combinations, all five multisine tone classes were transmitted repeatedly, with 750 samples recorded per condition and each sample labeled with its tone class. The antenna geometry, orientation, and line-of-sight placement were held fixed throughout, so the dataset cleanly characterizes the effects of transmitted power and path length on the harvested energy.

The resulting numbers trace a physically intuitive story. At the highest gain and shortest distance, mean harvested power reached 2.50 milliwatts with a maximum of 5.6 milliwatts, while at the lowest gain and longest distance the mean fell to just 0.26 milliwatts. Mean power declined monotonically with distance for every gain level, consistent with ordinary propagation loss, and higher gain consistently mitigated that loss. Perhaps more striking is the pattern tied to the waveforms themselves: median harvested power decreased monotonically as the tone count increased, from the highest values at two tones to the lowest at thirty-two, and this class separation persisted across every gain and distance setting. Because the tone classes remained well separated with limited overlap, even as absolute power fell, the measurements carry genuine discriminative information usable for classification.

To demonstrate that usability, the team ran baseline machine-learning experiments using multinomial logistic regression and support vector machines, with linear kernels at C equals 1 and radial basis function kernels at C equals 25 and gamma equal to 0.2. The authors are explicit that these classifiers serve as evidence that the data contain class-discriminative structure, not as records of the best achievable performance. What elevates the resource methodologically is its partitioning strategy. Dataset A, used for training, validation, and internal testing, contains measurements at the two higher gains and the three shorter distances, while dataset B is reserved entirely for external generalization testing at lower gains and longer distances. Models never saw dataset B during training, feature scaling, hyperparameter tuning, or model selection, so performance on B measures how well a classifier trained in one power-distance regime transfers to a different one.

The release follows FAIR² principles with unusual thoroughness. The data ship as machine-readable CSV files with a data dictionary, partitioning metadata, a JSON schema, provenance documentation, a reproducibility guide, and analysis scripts in Python 3.12 using scikit-learn, NumPy, pandas, and matplotlib. Metadata aligns with schema.org, W3C PROV-O, and the MLCommons Croissant specification, checksums allow third-party integrity verification, and the ODC-By v1.0 license permits unrestricted reuse with attribution. The certification process also documents known biases candidly: a single hardware platform, indoor short-range line-of-sight conditions, a fixed dipole geometry, and a single acquisition session without timestamps all constrain how far results can be generalized.

Those limitations are worth understanding before downloading. The stored values reflect the complete receiver chain rather than calibrated incident RF power, because the diode-based rectenna performs a nonlinear RF-to-DC conversion that is itself part of the measured response. At the lowest gain setting, where readings sit near the bottom of the receiver dynamic range, sensitivity limits and finite readout resolution may loom larger than at higher gains. There are no raw IQ captures or time-domain waveforms, so the dataset cannot support channel estimation or end-to-end deep learning directly from baseband samples, and environmental logs of temperature and humidity, though the harvesters carried the sensors, were excluded by design.

For the SWIPT community, the significance lies in reproducibility. Waveform design for simultaneous information and power transfer has produced a rich theoretical literature, but rigorous, openly shared experimental datasets that couple signal features to harvested energy under controlled hardware conditions remain scarce. By encoding information in tone count, capturing the nonlinear rectenna response, and partitioning the data for in-distribution and out-of-distribution evaluation, the Cyprus and Finnish team has created a resource intended for benchmarking tone-count classifiers, studying energy harvesting efficiency, and developing AI-enabled wireless systems that must do two jobs at once: talk and power their listeners.

Subject of Research: Experimental dataset from a multisine SWIPT system measuring tone-count classification and RF energy harvesting across transmitter gain and distance settings

Article Title: Multisine signal tone and power harvesting measurements across gain and distance settings

Article References: Stylianou, P., Faddoul, E., Selim Korium, M., & Krikidis, I. (2026). Multisine signal tone and power harvesting measurements across gain and distance settings. Frontiers in Communications and Networks, 7, Article 1911508. https://doi.org/10.3389/frcmn.2026.1911508

Image Credits: AI Generated

DOI: 10.3389/frcmn.2026.1911508

Keywords: SWIPT, wireless power transfer, multisine waveform, RF energy harvesting, rectenna, software-defined radio, machine learning, tone-index modulation, IoT, FAIR data, signal classification, University of Cyprus

The device, which the authors call a Funnel Morph Antenna, gets its name from a distinctive hybrid geometry that combines a narrow tapered neck with a flared upper section, resembling the profile of an industrial funnel. That shape is not merely aesthetic. In millimeter-wave antenna design, one of the central challenges is impedance matching: the antenna must present a consistent 50-ohm load to the feeding circuitry across a wide range of frequencies, or else a large fraction of the captured signal simply reflects back instead of being delivered to the rectifier. The funnel profile solves this by providing a gradual electromagnetic transition from the narrow neck, which concentrates surface currents toward the radiating aperture, to the wide flared portion, which extends the effective radiating area. Because the current path changes smoothly rather than abruptly, discontinuities that would normally cause reflections are minimized, and the antenna maintains a reflection coefficient below minus 10 dB across the full operating band.

The physics behind the wideband behavior is a story of overlapping resonances. Simulations of the surface current distribution at 28, 33, and 38 GHz reveal that different parts of the structure dominate at different frequencies. At 28 GHz, current concentrates around the narrow neck and lower radiating sections, corresponding to a low-frequency resonant mode. By 33 GHz, the current spreads into the flared upper portion, indicating the formation of additional resonant modes, and at 38 GHz the maximum current density shifts to the upper flare and the cavity regions, where shorter current paths support the highest-frequency operation. The superposition of these multiple modes is what stretches the usable bandwidth to a full 14 GHz, an unusually wide span for a compact millimeter-wave element. A multilayered cavity embedded in the ground plane adds further control, confining the electromagnetic fields and stabilizing the directional radiation pattern.

Perhaps the most provocative engineering decision in the study is the choice of substrate. Millimeter-wave devices almost always use specialized low-loss laminates such as Rogers RT/duroid, because ordinary FR4, the fiberglass material found in countless consumer circuit boards, suffers from significant dielectric losses and dispersion at frequencies above 26 GHz. The team deliberately chose 1.6-millimeter FR4, with a dielectric constant of 4.3 and a loss tangent of 0.02, to test whether a genuinely low-cost design could still perform. Despite the higher insertion losses, careful optimization of the flare and feed geometry allowed the antenna to achieve wideband matching, and the geometry proved robust against the etching tolerances of standard printed circuit board fabrication, holding its performance even with dimensional deviations of plus or minus 0.05 millimeters. The single element measures only about 17.5 by 20.3 millimeters and delivers a simulated peak gain of roughly 5 dBi with radiation efficiency above 85 percent, figures that measurements closely confirmed.

A single antenna, however, captures only a modest amount of power, so the researchers replicated the element into a four-element array. To feed all four elements uniformly, they designed a 1-to-4 Wilkinson power divider, a classic microwave component built from three cascaded two-way dividers, each using quarter-wavelength transmission lines with a characteristic impedance of twice the system impedance and a 100-ohm isolation resistor between output ports. The divider performed impressively across the band, with insertion loss between roughly minus 6 and minus 6.2 dB, meaning each output port receives almost exactly one quarter of the input power, and port-to-port isolation exceeding minus 45 dB around 30 GHz, indicating negligible signal leakage between channels. The array elements were spaced 5 millimeters apart, half a wavelength at the operating frequency, to maximize gain while suppressing unwanted side lobes.

The assembled array, measuring 78.25 by 70.57 millimeters, delivered a measured peak gain of 10.25 dBi and an efficiency of about 93 percent at 28 GHz. The theoretical maximum gain for four ideal elements with a single-element gain of 4.8 dBi would be 10.82 dBi, so the measured value falls only 0.57 dB short, a gap the authors attribute to feed network losses, conductor losses in the microstrip lines, and the dielectric losses inherent to FR4. The aperture efficiency, a measure of how effectively the physical area of the array is used, remained a steady 78.5 percent despite mutual coupling between elements and phase errors across the wide band. The beam pattern is notably narrow, with a half-power beamwidth of about 9 degrees at 28 GHz and 10 degrees at 38 GHz in one plane, and side lobe levels between minus 7 and minus 9 dB, giving the array a focused, directional sensitivity well suited to harvesting energy from distant base stations.

Converting captured radio waves into usable electricity falls to the rectifier, and here the team navigated some practical constraints. They selected the 1SS351 Schottky diode from ON Semiconductor, chosen because its electrical characteristics closely match the widely used HSMS-2852 diode, which has recently become obsolete. Using Keysight Advanced Design System software, the researchers modeled the diode with harmonic balance and S-parameter simulations to extract its input impedance, finding a value of 47.4 minus j20 ohms near 28 GHz. Matching that impedance to the antenna required careful design work. The team compared half-wave and full-wave rectifier topologies and found that the full-wave bridge significantly raises the imaginary component of the input impedance, which would degrade efficiency without elaborate matching. A matched half-wave rectifier using microstrip lines emerged as the practical choice, especially after lumped-element matching proved infeasible because it required component values, such as a 53-ohm resistor and a 2.7-nanohenry inductor, that are difficult to source off the shelf.

The full system was validated experimentally in an anechoic chamber, with a broadband vector signal generator driving a standard gain horn antenna as the transmitter and the fabricated rectenna prototype as the receiver, separated by one meter, comfortably within the far-field region for these frequencies. Both antennas were carefully polarization-aligned, and the incident power density was calibrated with a broadband power sensor before characterization. When the array harvested 68.7 microwatts of incident RF power, the system produced an output voltage of 250 millivolts across a 2-kilohm load, corresponding to an overall RF-to-DC conversion efficiency of 45.5 percent. That figure is competitive with, and in several respects superior to, prior millimeter-wave rectennas, many of which required expensive substrates, waveguide structures, or far higher input power levels to reach comparable efficiency.

The comparison with earlier work underscores why the low-cost approach matters. Previous millimeter-wave rectennas have relied on Duroid substrates, substrate-integrated waveguides, liquid crystal polymer packaging, on-chip CMOS integration, and even air-filled waveguide Fabry-Perot resonators. Some achieved higher peak gains or higher conversion efficiencies, such as a waveguide-fed 35 GHz design reaching 68.5 percent, but typically with narrower bandwidth, more complex manufacturing, or substantially greater incident power. Others, like CMOS folded dipoles, achieved extreme miniaturization at the cost of negative gain. The new design occupies a distinctive middle ground: a full 26 to 40 GHz operating range, a respectable 10.25 dBi gain, a narrow 9-degree beamwidth, and 45.5 percent conversion efficiency, all on ordinary FR4 fabricated with standard PCB technology.

The implications reach beyond the laboratory. As 5G and future 6G networks densify urban radio environments, the ambient millimeter-wave energy available for harvesting will only grow, and devices that can scavenge it without batteries could power IoT sensors, wearables, and backscatter tags indefinitely. The authors suggest that future work will explore miniaturization techniques and adaptive beamforming to improve integration into compact real-world devices. If battery production costs, which are high relative to the energy those batteries actually store, continue to drive the push toward green electronics, a funnel-shaped patch of fiberglass that drinks from the 5G spectrum may prove to be one of the more elegant shortcuts to energy-autonomous wireless devices.

Subject of Research: A wideband funnel-geometry rectenna array for radio-frequency energy harvesting in the 26–40 GHz 5G millimeter-wave band

Article Title: Wideband funnel geometry rectenna array for RF energy harvesting in 26–40 GHz 5G networks

Article References: Megahed, A. A., Mousa, M. E., Al-Gburi, A., & Elabd, R. H. (2026). Wideband funnel geometry rectenna array for RF energy harvesting in 26–40 GHz 5G networks. Results in Optics, 25, Article 101178. https://doi.org/10.1016/j.rio.2026.101178

Image Credits: AI Generated

DOI: Not provided

Keywords: rectenna, RF energy harvesting, 5G, millimeter-wave, antenna array, Wilkinson power divider, Schottky diode, FR4 substrate, wireless power transfer, impedance matching, IoT, Results in Optics