1 Throughput Fairness-Aware Optimization of Cognitive Backscatter Networks with Finite Alphabet Inputs
1 Throughput Fairness-Aware Optimization of Cognitive Backscatter Networks with Finite Alphabet Inputs
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DOI：
10.1109/iccc55456.2022.9880808
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Xiaona Gao;Yinghui Ye;G. Lu;Haijian Sun
中科院分区：
文献类型：
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作者：
Xiaona Gao;Yinghui Ye;G. Lu;Haijian Sun
认知反向散射网络（Cognitive Backscatter Network，CBN）是能量受限的物联网网络的一种有前景的范例，在该网络中，反向散射设备（Backscatter Devices，BDs）从主信号中获取能量，并将信息反向散射给协作接收器。传统的最大化吞吐量的资源分配方案基于不切实际的高斯输入和能量收集（Energy Harvesting，EH）模型，这会导致性能下降。考虑到上述因素，本文重点研究具有有限字符输入和非线性EH模型的多用户CBN的公平感知资源分配方案。我们制定了一个最大最小吞吐量最大化问题，以确保BDs之间的公平性，同时满足主用户的服务质量（Quality - of - Service，QoS）和BDs的能量因果关系约束。由于所制定的联合优化问题是非凸的，我们使用近似、松弛和辅助变量方法将其转化为凸问题，并提出一种迭代算法来解决它。提供了仿真结果以验证所提算法的有效性。
Cognitive backscatter network (CBN) is a promising paradigm for energy-constrained IoT networks, in which backscatter devices (BDs) harvest energy from the primary signals and backscatter information to a cooperative receiver. Traditional resource allocation schemes maximizing the throughput are based on the impractical Gaussian inputs and energy harvesting (EH) model, which leads to performance degradation. Taking the above factors into account, this paper focuses on the fairness-aware resource allocation scheme for multiuser CBN with finite-alphabet inputs and nonlinear EH model. We formulate a max-min throughput maximization problem to ensure the fairness among BDs, subject to the quality-of-service (QoS) of primary user and energy-causality constraints of BDs. As the formulated joint optimization problem is non-convex, we use the approximation, slack and auxiliary variables methods to transform it into a convex problem and propose an iterative algorithm to solve it. Simulation results are provided to verify the effectiveness of the proposed algorithm.