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The evaluation of mathematical functions is a critical task in several hardware designs, and piecewise polynomial approximation (PPA) is one of the main techniques widely used for function evaluation. This technique employs uniform and non-uniform segmentation for splitting the function and approaching each function segment via polynomial approximation, where lookup tables are needed to store the polynomial coefficients. Today, several hardware-based PPA implementations have address decoder units with a considerable amount of hardware complexity. To face this problem, this paper proposes a new methodology that searches the optimal function segments, polynomial coefficients, and their representation in fixed-point format when the design is constrained to a decoder word-length. Thus, a second-order PPA evaluation architecture based on the Horner’s rule and a simplified address decoder unit with reduced complexity are provided. The proposed methodology uses non-uniform segmentation relying on a linear combination of power-of-two, which results in a reduction of the number of segments and consequently in the hardware complexity of the address decoder unit. The figures of merit employed by the auto-tuning (self-adapting) segmentation process are the first-order derivative (slope) of the function and the signal to quantization noise ratio quality signal metric. Experimental results show a hardware reduction of the decoder design (number of segments, polynomial coefficient ROMs) when is compared with state-of-the-art proposals.

Plant–Microbial Fuel Cell (P–MFC) is a renewable power source which generates bioelectricity through the plant-microbe interrelationship at the rhizosphere region of a plant. As a step toward sustainable wireless sensing, PMFC can harness the metabolism of microorganism as a catalyst and use organic matter to generate electrical energy. However, these energy sources tend to produce low power outputs (mW), and the P–MFC energy capacity can be affected by perturbations. The P–MFC needs to be analyzed for an accurate integration in harvester circuits and wireless technologies, such as LPWAN, to develop sustainable wireless communication applications. In this study, a P–MFC array is implemented as a promising self-sustainable green energy communication technology for Internet of Things (IoT)-based wireless sensor network (WSN). The serial-parallel configuration of the Dypsis lutescens plant type is characterized and adapted with an energy harvester (EH) circuit which manages the energy from P–MFC. An energy capacity model is proposed to analyze the supercapacitor’s charge response and the recovery effect after each Long-Range (LoRa) transmission of the sensor node. The integration of a power management strategy is used to improve the sustainable operation in each sensor node. Experimental results show an open circuit voltage of 1.75 V, and a short current circuit of 5.6 mA for the serial-parallel configuration of the P–MFC array. The test-scenarios demonstrate sustainable operation of the LoRa-WSNs after one month with a daily generation capacity of 80 J, which is sufficient for the WSN’s node operation.

Wireless sensor networks (WSNs) are technologies that play an important role in Internet of Things (IoT) systems. In most applications, sensor nodes are expected to operate for extended periods without maintenance. Therefore, minimizing power consumption and self-sustainability based on energy harvesting (EH) still represent important research challenges. In this article, a power management strategy (PMS) based on the weighted order statistics (WOS) classification technique is proposed to dynamically adapt the duty cycle of the sensor node according to historical data measurements. Unnecessary acquisition and transmission of slow-varying signals are reduced, improving the power consumption of the node. As traditional renewable sources (sunlight, wind, and vibration) are scarce in indoor scenarios, an array of Dypsis Lutescens plants is used as a power bioenergy cell, providing a clean and cost-effective alternative to power indoor sensor nodes. The WOS technique is programmed into an nRF52840 microcontroller, and an ultralow-power BQ25570 harvesting circuit harnesses the energy from the array of plants. Experimental results include the energy performance analysis of the wireless sensor node and the measured power generation capacity of the bioenergy source, showing an energy-autonomous behavior for IoT applications. Average power consumption of 15.6 mW per transmitted data packet of 14 bytes is achieved, which represents the ability to perform at least 693 data transmissions per day considering an 8 F supercapacitor as a storage device.

Localization is one of the major problems in mobile wireless sensor networks, since it provides the location of an event occurrence. This paper evaluates the performance of range-based localization algorithms: the multilateration algorithm, the weighted multilateration algorithm, the maximum likelihood algorithm and the multilateration algorithm with a correcting factor. We also propose the weighted multilateration algorithm with a correcting factor through which the localization error is decreased through the addition of a factor that corrects the distance between the node of interest and the reference nodes. Our proposal includes the analysis of noise in the environment due to the time of arrival and the analysis of node of interest’s mobility in the calculation of the correlation matrix of the weighted multilateration algorithm and the calculation of the correcting factor. The localization algorithms analyzed in this study are evaluated in a single-hop scenario and a multi-hop scenario considering a well-defined distribution of the reference nodes and a random distribution of these in both scenarios. The results we obtained varying the number of reference nodes and noise proportion prove that the algorithm we propose yields a better performance than the other analyzed algorithms do, according to the normalized root mean squared error.

The design of digital hardware controllers for the integration of renewable energy sources in DC microgrids is a research topic of interest. In this paper, a Kalman filter-based maximum power point tracking algorithm is implemented in an FPGA and adapted in a dual active bridge (DAB) converter topology for DC microgrids. This approach uses the Hardware/Software (HW/SW) co-design paradigm in combination with a pipelined piecewise polynomial approximation design of the Kalman-maximum power point tracking (MPPT) algorithm instead of traditional lookup table (LUT)-based methods. Experimental results reveal a good integration of the Kalman-MPPT design with the DAB-based converter, particularly during irradiation and temperature variations due to changes in weather conditions, as well as a good balanced hardware design in complexity and area-time performance compared to other state-of-art FPGA designs.

Many applications such as gaming, digital signal processing and communications systems, require computation of the reciprocal square root operation (RSR). Although several architectures have been reported for computing the RSR operation, these are mainly focused on accelerating high-precision floating-point units. In mobile-device implementations, fixed-point (FxP) units are preferred due to their low computational cost and power consumption. This article presents an on-chip implementation of a bit-accurate, FxP-RSR unit using a 130 nm CMOS process. The proposed architecture is based on a piecewise-polynomial approximation in a reduced range of the RSR function and the Newton-Raphson method. Experimental results show that the manufactured chip exhibits lower latency and less power consumption than existing standard-cell-based implementations. These characteristics make the proposed chip a useful silicon intellectual property suitable for embedded applications where low power, low latency, and low hardware cost is required.

A novel geometry-based statistical model for non-stationary multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) Rayleigh fading channels is presented in this paper. The proposed model seeks to provide new insights into the non-stationary characteristics of MIMO V2V channels stemming from the time-varying propagation delays of the received multipath signal. To achieve this goal, the temporal evolution of the propagation delays is characterized following a simple, although theoretically sound, plane-wave propagation approach. The modelling framework presented here allows obtaining generic expressions for the space-time-frequency cross-correlation function of the MIMO V2V channel, unveiling in the process important features of the channel's correlation and stationary properties. The obtained results show that as a consequence of the time-varying character of the propagation delays, the elements of the MIMO channel matrix are non-wide-sense stationary uncorrelated scattering (non-WSSUS) ran...

Recently, there has been an increase in the development of new intelligent transportation systems. Specifically, this technology was proposed with the aim of increasing road safety and improving traffic flow management by information interchange among vehicles. In this sense, the dedicated radio short-range communication (DSRC) technology enables reliable vehicle-to-vehicle (V2V) and vehicleto-infrastructure (V2I) communication. However, in order to guarantee the correct operation of the radio transmission systems, it is necessary to test the transmitter and receiver devices under controlled conditions. A channel emulator offers a fast and non-expensive solution to perform these tests. This paper presents a new non wide-sense stationary uncorrelated scattering (non-WSSUS) channel emulator for vehicle communications, which is based on software defined radios (SDR). Implementation results show that the proposed channel emulator can reproduce propagation environment corresponding to non-WSSUS channels with exactness. This will allow testing of communications systems under development using the SDR-based V2V-V2I channel emulator.

This paper describes the implementation of a narrowband sounder for Doppler power spectrum (DPS) measurements of vehicular communication channels. The narrowband channel sounder is implemented using general purpose equipment, making such measurement platform easy to replicate for didactic and research purposes. To demonstrate the practical value of this framework, a measurement campaign was conducted to obtain empirical information about the spectral characteristics of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) multipath radio channels in the 700 MHz band. The collected data were processed to compute the average Doppler shift and the Doppler spread of the measured channels. The obtained results show that the spectral properties of frequency-dispersive vehicular radio channels can be effectively analyzed using narrowband sounding principles. RESUMEN Este trabajo describe la implementación de un sondeador de banda angosta para mediciones de espectro de potencia Doppler (EPD) de canales de comunicación vehicular. El sondeador de canal de banda angosta es implementado usando equipo de propósito general, por lo que esta plataforma de medición puede ser reproducida fácilmente para propósitos de docencia e investigación. Para ejemplificar el funcionamiento de la plataforma, se llevó a cabo una campaña de mediciones del EPD de canales de comunicación de vehículo a vehículo (V2V, por sus siglas en inglés) y de vehículo a infraestructura (V2I, por sus siglas en inglés) en la banda de 700 MHz. Los datos recolectados fueron procesados para obtener los momentos estadísticos relevantes del EPD de los canales medidos, es decir, el desplazamiento Doppler promedio y la dispersión Doppler. Los resultados obtenidos muestran que las características espectrales de los canales de comunicación vehicular con dispersión en frecuencia pueden ser investigadas usando principios de sondeo de banda angosta.

The hardware of the matrix inversion architecture using QR decomposition with Givens Rotations (GR) and a back substitution (BS) block is required for many signal processing algorithms. However, the hardware of the GR algorithm requires the implementation of complex operations, such as the reciprocal square root (RSR), which is typically implemented using LookUp Table (LUT) and COordinate Rotation DIgital Computer (CORDICs), among others, conveying to either high-area consumption or low throughput. This paper introduces an Field-Programmable Gate Array (FPGA)-based full matrix inversion architecture using hybrid piecewise polynomial approximation systolic cells. In the design, a hybrid segmentation technique was incorporated for the implementation of piecewise polynomial systolic cells. This hybrid approach is composed by an external and internal segmentation, where the first is nonuniform and the second is uniform, fitting the curve shape of the complex functions achieving a better signal-quantization-to noise-ratio, furthermore, it improves the time performance and area resources. Experimental results reveal a well-balanced improvement in the design achieving high throughput and, hence, less resource utilization in comparison to state-of-the-art FPGA-based architectures. In our study, the proposed design achieves 7.51 Mega-Matrices per second for performing 4 &times, 4 matrix operations with a latency of 12 clock cycles, meanwhile, the hardware design requires only 1474 slice registers, 1458 LUTs in an FPGA Virtex-5 XC5VLX220T, and 1474 slice registers and 1378 LUTs when a FPGA Virtex-6 XC6VLX240T is used.

Nowadays, wireless channel emulators are designed for channel models that require the evaluation of logarithmic, trigonometric, exponential, and other transcendental functions. These channel emulators are used for testing wireless communication standards associated to Weibull, Suzuki, Nakagami, Rayleigh and Gaussian distributions. The piecewise polynomial approximation (PPA) technique allows the evaluation of functions with an accuracy level that depends on the used segmentation method; being the uniform and non-uniform by the power of two, the most commonly employed. However, these segmentation techniques lack the required flexibility to be useful for any function since the signal to quantization noise ratio (SQNR) is highly dependent on the function at hand. A new function segmentation methodology is presented based on the first and second derivative. Simulation results show significant SQNR advantages when the proposed method is compared with state-of-art segmentation techniques.