GECOS Lab, National School of Applied Sciences, Cadi Ayyad University, 40000 Marrakech, Morocco.
Department of Signal Theory and Communications, University Carlos III of Madrid, Leganés, 28911 Madrid, Spain.
Sensors (Basel). 2018 Dec 31;19(1):116. doi: 10.3390/s19010116.
Nowadays, the sensor community has become wireless, increasing their potential and applications. In particular, these emerging technologies are promising for vehicles' communications (V2V) to dramatically reduce the number of fatal roadway accidents by providing early warnings. The ECMA-368 wireless communication standard has been developed and used in wireless sensor networks and it is also proposed to be used in vehicular networks. It adopts Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) technology to transmit data. However, the large power envelope fluctuation of OFDM signals limits the power efficiency of the High Power Amplifier (HPA) due to nonlinear distortion. This is especially important for mobile broadband wireless and sensors in vehicular networks. Many algorithms have been proposed for solving this drawback. However, complexity and implementations are usually an issue in real developments. In this paper, the implementation of a novel architecture based on multilayer perceptron artificial neural networks on a Field Programmable Gate Array (FPGA) chip is evaluated and some guidelines are drawn suitable for vehicular communications. The proposed implementation improves performance in terms of Peak to Average Power Ratio (PAPR) reduction, distortion and Bit Error Rate (BER) with much lower complexity. Two different chips have been used, namely, Xilinx and Altera and a comparison is also provided. As a conclusion, the proposed implementation allows a minimal consumption of the resources jointly with a higher maximum frequency, higher performance and lower complexity.
如今，传感器领域已经实现了无线化，这极大地增加了它们的潜力和应用范围。特别是，这些新兴技术有望应用于车辆通信（V2V），通过提供早期预警，显著减少道路交通事故的死亡人数。ECMA-368 无线通信标准已经被开发并应用于无线传感器网络，并且也被提议应用于车联网。它采用多带正交频分复用（MB-OFDM）技术来传输数据。然而，OFDM 信号的大功率包络波动限制了高功率放大器（HPA）的功率效率，因为其会导致非线性失真。这对于移动宽带无线和车联网中的传感器来说尤为重要。已经提出了许多算法来解决这个缺点。然而，在实际开发中，复杂度和实现通常是一个问题。在本文中，评估了基于多层感知器人工神经网络的新型架构在现场可编程门阵列（FPGA）芯片上的实现，并为车联网通信提出了一些合适的指导原则。所提出的实现方案在峰均功率比（PAPR）降低、失真和误码率（BER）方面提高了性能，同时复杂度也大大降低。使用了两种不同的芯片，即 Xilinx 和 Altera，并提供了比较。总之，所提出的实现方案允许在最小资源消耗的情况下，实现更高的最大频率、更高的性能和更低的复杂度。
