MIGOU: A Low-Power Experimental Platform with Programmable Logic Resources and Software-Defined Radio Capabilities

Abstract

The increase in the number of mobile and Internet of Things (IoT) devices, along with the demands of new applications and services, represents an important challenge in terms of spectral coexistence. As a result, these devices are now expected to make an efficient and dynamic use of the spectrum, and to provide processed information instead of simple raw sensor measurements. These communication and processing requirements have direct implications on the architecture of the systems. In this work, we present MIGOU, a wireless experimental platform that has been designed to address these challenges from the perspective of resource-constrained devices, such as wireless sensor nodes or IoT end-devices. At the radio level, the platform can operate both as a software-defined radio and as a traditional highly integrated radio transceiver, which demands less node resources. For the processing tasks, it relies on a system-on-a-chip that integrates an ARM Cortex-M3 processor, and a flash-based FPGA fabric, where high-speed processing tasks can be offloaded. The power consumption of the platform has been measured in the different modes of operation. In addition, these hardware features and power measurements have been compared with those of other representative platforms. The results obtained confirm that a state-of-the-art tradeoff between hardware flexibility and energy efficiency has been achieved. These characteristics will allow for the development of appropriate solutions to current end-devices’ challenges and to test them in real scenarios.

Software Defined Radio Platforms for Wireless Technologies

“…It becomes necessary to build mobile SDR platforms consuming less energy and at the same time offer expected performance. One discussed solution in literature [42], [115], [116] is to use low power embedded computer boards such as Raspberry Pi, ARM Cortex processor, etc., as GPP hosts. Nevertheless, prototypes based on this solution lack offering adequate performance to support the requirement (specially, the frequency, bandwidth, latency) of most wireless technologies.…”

Gated Recurrent Unit Neural Networks for Automatic Modulation Classification With Resource-Constrained End-Devices

“…For this reason, in this work we have generated a new dataset with wireless signals recorded in a real environment directly by a resourceconstrained SDR end-device. Specifically, we have used MIGOU [11], a low-power experimental platform with SDR capabilities that has been specifically designed to address the hardware architectural constraints that limit CR research and experimentation with low-power end-devices. This platform was configured to sense a communication channel and send the raw I/Q samples (without timing recovery) to a computer that properly stores them.…”

“…AMC methods require the use of Software-Defined Radios (SDRs) to acquire the raw radio signals they need as input. However, low-power SDR end-devices have certain acquisition limitations compared to Universal Software Radio Peripherals (USRPs) and other widely used traditional SDR systems [9]- [11]. The complexity of AMC methods must therefore be reduced to adapt their requirements to the memory and processing resources of end-devices.…”

“…The main contributions of this work include: 1) A publicly available dataset consisting of over-the-air measurements of real radio signals with 11 different modulations [12]. These measurements were acquired with a resource-constrained SDR end-device [11], which is the type of platform targeted by this work. Specifically, the signals were recorded in an office environment at two different distances between the transmitter and the receiver, 1 and 6 meters, so their amplitude, noise level, and propagation conditions are different.…”

Cross-layer strategies for improving the quality of service of wireless sensor networks

“…This reception mode is very energy-demanding, causing power consumptions in the same order of magnitude as those related to transmission. Radio transceivers generally have a sleep mode in which their power consumption is reduced by several orders of magnitude in relation to their transmission, reception or idle states [131]. Thus, minimizing the number of transmissions and putting the transceiver in this low-power sleep mode whenever possible achieves a significant reduction in energy consumption [28].…”