Ambient Backscatter Systems: Exact Average Bit Error Rate Under Fading Channels
Ambient Backscatter Systems: Exact Average Bit Error Rate Under Fading Channels
复制标题
DOI：
10.1109/tgcn.2018.2880985
复制
发表时间：
2018-04
影响因子：
4.8
通讯作者：
J. K. Devineni;Harpreet S. Dhillon
中科院分区：
文献类型：
--
作者：
J. K. Devineni;Harpreet S. Dhillon
物联网（IoT）模式的成功依赖于开发节能的通信技术，使数十亿个电池供电的物联网设备之间能够进行信息交换。环境后向散射以其同时传输信息和能量的技术能力，迅速成为这种通信模式的一种有吸引力的解决方案，特别是对于低数据速率要求的链路。本文研究了环境后向散射系统的信号检测和精确误码率特性。特别地，我们在接收端提出了一个二元假设检验问题，并分析了三种检测技术下的系统性能：1)平均阈值；最大似然阈值（MLT）；3)近似MLT。由于物联网设备的能量限制性质，我们对两种接收器类型进行了上述分析：1)可以准确跟踪信道状态信息的接收器和2)不能准确跟踪信道状态信息的接收器。将本文与现有技术区分开来的分析的两个主要特征是平均接收信号能量的精确条件密度函数的表征，以及该设置的精确平均误码率的表征。关键的挑战在于处理两个假设的信道增益之间的相关性，从而推导出误码率分析所需的信道增益平方幅度的联合概率分布。
The success of Internet-of-Things (IoT) paradigm relies on, among other things, developing energy-efficient communication techniques that can enable information exchange among billions of battery-operated IoT devices. With its technological capability of simultaneous information and energy transfer, ambient backscatter is quickly emerging as an appealing solution for this communication paradigm, especially for the links with low data rate requirement. In this paper, we study signal detection and characterize exact bit error rate (BER) for the ambient backscatter system. In particular, we formulate a binary hypothesis testing problem at the receiver and analyze system performance under three detection techniques: 1) mean threshold; 2) maximum likelihood threshold (MLT); and 3) approximate MLT. Motivated by the energy-constrained nature of IoT devices, we perform the above analyzes for two receiver types: 1) the ones that can accurately track channel state information and 2) the ones that cannot. Two main features of the analysis that distinguish this paper from the prior art are the characterization of the exact conditional density functions of the average received signal energy, and the characterization of exact average BER for this setup. The key challenge lies in the handling of correlation between channel gains of two hypotheses for the derivation of joint probability distribution of magnitude squared channel gains that is needed for the BER analysis.