Next-generation ultrawideband (UWB) applications require high-performance and fully integrated UWB antenna systems to guarantee accurate localization and sensing in challenging Internet of Things (IoT) environments. This article proposes an entire system-level simulation framework that accelerates the design and optimization of integrated UWB antenna systems for various IoT applications by accurately predicting the effects of the entire system integration environment and enabling time-efficient optimization of system-level metrics. These metrics include the system fidelity factor and the distance estimation error in full 3-D, which are required to minimize orientation-specific pulse distortion and phase-center variation. To reconcile fast and accurate system-level performance prediction with reduced computational resources, the UWB link is partitioned, enabling the combination of standalone full-wave antenna simulations, UWB front-end circuit models, and UWB wireless channel models considering all antenna and circuit imperfections. Moreover, this simulation framework is the first to include the Huygens’ field equivalence principle to efficiently and accurately model the entire system environment, crucial to ensure high-performance UWB antenna systems for integrated IoT applications. To validate the simulation framework, an extensive time-domain measurement campaign was performed on a representative UWB link, including multiple integration platforms. Simulation and measurement results correspond well and show that the presence of the actual integration platform significantly impacts the system performance along different orientations. The simulation framework is several orders of magnitude faster than what is currently achievable with conventional electromagnetic field simulators and facilitates the development of high-performance and fully integrated UWB systems that satisfy the needs of demanding IoT applications.

Driven by the increasing demand for data, connectivity and automation, the amount of Internet of Things (IoT) devices continues to expand across consumer electronics and industrial applications. Integrating energy harvesting (EH) technologies as a battery-free alternative requires a controlled setting to emulate realistic EH systems. Yet, current tools are often not sufficiently accurate to model all hardware and software components of an EH system. Moreover, it is hard to mimic realistic energy availability scenarios, which are crucial for the design and optimization of EH systems. Therefore, a novel emulation platform is introduced that facilitates the development, testing, and optimization of complete and realistic EH systems. This emulator device replicates the behavior of the entire EH system. It consists of a Raspberry Pi 5 with a custom developed add-on hardware hat and dedicated software. This add-on hardware hat provides a stable output voltage between 1.2 and 3.6 V to the IoT device under test and contains a current measurement circuit with an accuracy of$0.6~\mu $A. The software includes novel and accurate digital-twin models of the energy harvester, the storage element, and the power management unit, enabling the emulation of various EH scenarios. Two application scenarios are demonstrated, a wireless bluetooth low energy (BLE) heart rate sensor optimized for ambient light EH and a compact wireless BLE temperature sensor optimized for radio-frequency EH. The proposed emulation platform enables rapid EH design evaluation under varying energy conditions, streamlining development and validating system reliability. This could be an important step toward establishing EH as a sustainable and widely adopted alternative to conventional battery-powered systems.

Although first proposed a few decades ago, chirp-based modulation has recently seen a surge of popularity as a result of its application in the LoRa standard. Over the past years, this key Internet of Things (IoT) enabler has been well researched, and ever more advanced low-power wide-area networks (LPWANs) are being implemented across the globe, based on this technology. As a result of this international acclaim, multiple actors have invested efforts into implementing frequency-shift chirp modulation (FSCM), which is a more general term for the open-source physical layer modulation protocol also embedded in the LoRa standard, on software-defined radio (SDR) systems. However, while oversampling, advanced post-processing and other digital techniques have led to significant advances in the technology’s capabilities and reliability, real-world deployments of these SDR implementations have to overcome the excessive computational cost associated to these techniques. In response to this challenge, this article examines several new strategies for symbol detection methods operating on FSCM signals, such as those employed in LoRa modulation, enabling significant computational cost reductions. Examples of these are integrating frequency correction in the dechirping procedure and omitting the downsampling operation by using upsampled down-chirps when processing the received samples. In comparison to the standard detection method, computational efficiency gains between 19% and 36% are achieved. Hence, applying the methods presented in this work can yield significant reductions in power consumption for real-world SDR-based FSCM systems in state-of-the-art IoT deployments.

In recent years, LoRa has been deployed in countless Internet of Things (IoT) applications across the globe. However, as LoRa is a proprietary technology, research into its physical-layer performance has been challenging. Implementing LoRa on software-defined radio (SDR) platforms yields valuable insight into the physical layer of the LoRa standard and paves the way for improvements in packet reception capabilities for LoRa receivers. This article presents an independently developed packet reception algorithm, which drastically improves the physical performance of LoRa communication links. The advanced signal presence detection, synchronization, and symbol detection strategies are shown to significantly increase packet reception ratios in extremely adverse noise conditions. Multiple algorithm variations are presented and compared in terms of bit error rate (BER) performance and computational cost. In comparison to a theoretical system with perfect channel state information, the simulated BER performance of the best performing algorithm only requires an increase of 1.6 dB in signal-to-noise ratio (SNR) to exhibit the same performance. Finally, SDR implementations of the algorithms exhibit average SNR performance gains up to 4.7 dB when compared to commercially available hardware.

We consider uplink multiuser multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication. The transmit (Tx) side of the envisaged system consists of several single-antenna users or/and several multiple-antenna users. At the receive side, a multiple-antenna access point employs compressive sensing techniques to estimate the channel impulse response from the preamble portion of the observed packets. The traditional approach is that of orthogonal pilot allocation: during a short training period, each OFDM subcarrier is assigned exclusively to a single Tx antenna. In this case, the channel state information can conveniently be acquired on a per Tx antenna basis. To the best of our knowledge, all related research imposes that all Tx antennas are allocated the same amount of pilots (which must then be tailored for the most extreme channel conditions). However, in the considered system, Tx antennas may experience totally different channel conditions. Under these circumstances, the use of a fixed number of pilots per Tx antenna results in a lot of unnecessary overhead. To tackle this problem, our work addresses the design of efficient algorithms for adaptive orthogonal pilot allocation. The following design principles are applied: orthogonal pilot allocation, constant-modulus modulation, minimum measurement matrix mutual coherence optimization, and the condition that the number of pilot subcarriers allocated to each Tx antenna is adjusted to the channel conditions experienced by that Tx antenna. The paper tackles the problem of determining the optimal number of pilot subcarriers as well as the optimal positions of the pilots. To facilitate adaptive operation, we propose a reduced-complexity method to determine the optimal pilot positions. The performance of our algorithms is demonstrated by means of computer simulations, using both theoretical channel models and results from our own channel measurement campaign.

In-band full-duplex (FD) wireless communication allows the simultaneous transmission and reception of data at the same frequency band, effectively doubling the spectral efficiency and data rate while reducing the latency. Previously published designs mostly target the self-interference (SI) cancellation in conventional wireless systems. In this paper, we focus on real-time SI cancellation for short-reach wireless FD systems. The superior signal quality of a point-to-point short-reach wireless system, allows the utilization of wideband communications to achieve a high throughput. Besides, in such wireless systems, the impacts of phase noise and nonlinear distortions are largely reduced, easing the SI cancellation. Moreover, the degradation of signal reception quality due to FD operation is experimentally evaluated in different environments. Experimental results of a prototype implementation show that a combination of antenna isolation and digital cancellation can already achieve an overall SI cancellation performance of 72.5 dB over a bandwidth of 123 MHz. This prototype can support a high-data-rate FD communication link of close to 1 Gbps up to 300 cm with an error vector magnitude lower than -26 dB in a typical indoor environment.

For the first time, a wearable personal distributed exposimeter (WPDE) is designed and calibrated for the Long-Term Evolution (LTE) 800 and 2600 MHz downlink bands. The proposed WPDE has a 68% confidence interval of 4.8–5.6 dB for different number of antennas and polarizations. Measurements of the WPDE are compared and validated with a commercial exposimeter in a real environment.

Smart-fabric interactive-textile systems offer exciting new possibilities, provided that they exhibit sufficient robustness and autonomy to be reliably deployed in critical applications. Textile multiantenna systems, unobtrusively integrated in a professional garment, are key components of such systems, as they set up energy-efficient and stable wireless body-centric communication links. Yet, their functionality may be further extended by exploiting their surface as energy-harvesting platform. Different state-of-the-art energy harvesters are suitable for compact integration onto a textile antenna. We demonstrate this by integrating a power management system, together with multiple diverse scavenging transducers and a storage module, on a well-chosen textile antenna topology. We provide guidelines to ensure that the additional hardware does not affect the textile antenna's performance. Simultaneous scavenging from different energy sources significantly increases the autonomy of a wearable system, in the meanwhile reducing battery size.

Indoor off-body wireless MIMO links between a mobile user equipped with wearable textile patch antennas and a fixed base station exhibit specific channel behavior due to the near presence and movements of the human body. Therefore, they require a dedicated channel model that captures the effects of correlated small-scale Rayleigh fading and correlated lognormal shadowing. A methodology is presented to construct such a model, allowing to predict the bit error characteristics and channel capacity curves based on the shadowing and fading correlation matrices that are extracted from channel measurements. It is shown that by separating shadowing, including effects caused by movement and reorientation of the human body, from small-scale fading, the main mechanisms of the off-body communication link are accurately captured by the model. A clear dependence of the shadowing correlation values on the physical layout of the antenna system is found. In our measurements, shadowing is not significantly decorrelated by polarization diversity or front-to-back diversity whereas the small-scale fading is clearly decorrelated. From the model, MIMO channel realizations with identical bit error rate and channel capacity characteristics as the measured channel can be quickly generated for link emulation purposes.

We present a new spherical ESPRIT algorithm, being a closed-form algorithm for use in conjunction with spherical arrays that provides automatically paired source azimuth and elevation estimates. It is a numerically efficient 2D angle-of-arrival estimation algorithm which does not rely on extensive spectral searches or iterative solutions to multi-dimensional optimization problems. The spherical ESPRIT is based on the spherical phase mode excitation principle, enabling us to exploit the spherical symmetry of the antenna array by relying on a recursive relationship between spherical harmonics.

A two-dimensional mixed-potential integral equation formulation is used to analyze the eigenmodes of microstrip lines. The new method involves a fast evaluation scheme for the space domain Green's functions of the substrate by using perfectly matched layers to obtain closed-form expressions as a series of leaky and Berenger modes. Efficient summation of these series is performed by means of the Shanks transform. The modal series can be applied to calculate all field interactions analytically, except for the selfpatch and the nearest neighbor contributions. Examples show an important reduction in CPU time for the new perfectly matched layers approach, as compared to the classical evaluation of the continuous Sommerfeld integrals.