Symbiotic radio (SR) is emerging as a spectrum-and energy-efficient communication paradigm for future passive Internet of Things (IoT), where some single-antenna backscatter devices, referred to as Tags, are parasitic in an active primary transmission. The primary transceiver is designed to assist both direct-link (DL) and backscatter-link (BL) communication. In multi-Tags SR systems, the transceiver designs become much more complicated due to the presence of DL and inter-Tag interference, which further poses new challenges to the availability and reliability of DL and BL transmission. To overcome these challenges, we formulate the stochastic optimization of transceiver design as the general network utility maximization problem (GUMP). The resultant problem is a stochastic multiple-ratio fractional nonconvex problem, and consequently challenging to solve. By leveraging some fractional programming techniques, we tailor a surrogate function with the specific structure and subsequently develop a batch stochastic parallel decomposition (BSPD) algorithm, which is shown to converge to stationary solutions of the GNUMP. The simulation results verify the effectiveness of the proposed algorithm by numerical examples in terms of the achieved system throughput.

中文翻译：

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多标签共生无线电系统中的随机收发器优化

共生无线电（SR）正在成为未来无源物联网（IoT）的频谱和节能通信范例，其中一些单天线反向散射设备（称为标签）在有源一次传输中是寄生的。主收发器旨在协助直接链路（DL）和反向散射链路（BL）通信。在多标签SR系统中，由于DL和标签间干扰的存在，收发器的设计变得更加复杂，这进一步对DL和BL传输的可用性和可靠性提出了新的挑战。为了克服这些挑战，我们将收发器设计的随机优化公式化为通用网络效用最大化问题（GUMP）。由此产生的问题是随机的多比率分数非凸问题，因此很难解决。通过利用一些分数编程技术，我们为具有特定结构的代理函数量身定做，随后开发了批处理随机并行分解（BSPD）算法，该算法被证明可收敛于GNUMP的固定解。仿真结果通过数值实例验证了所提算法的有效性。