This study introduces and investigates the integration of a cell-free architecture with bistatic backscatter communication (BiBC), referred to as cell-free BiBC or distributed access point (AP)-assisted BiBC, which can enable potential applications in future (EH)-based Internet-of-Things (IoT) networks. To that purpose, we first present a pilot-based channel estimation scheme for estimating the direct, cascaded, forward channels of the proposed system setup. We next utilize the channel estimates for designing the optimal beamforming weights at the APs, reflection coefficients at the tags, and reception filters at the reader to maximize the tag sum rate while meeting the tags' minimum energy requirements. Because the proposed maximization problem is non-convex, we propose a solution based on alternative optimization, fractional programming, and Rayleigh quotient techniques. We also quantify the computational complexity of the developed algorithms. Finally, we present extensive numerical results to validate the proposed channel estimation scheme and optimization framework, as well as the performance of the integration of these two technologies. Compared to the random beamforming/combining benchmark, our algorithm yields impressive gains. For example, it achieves $\sim$ 64.8\% and $\sim$ 253.5\% gains in harvested power and tag sum rate, respectively, for 10 dBm with 36 APs and 3 tags.

翻译：本研究引入并探讨了无蜂窝架构与双站反向散射通信（BiBC）的融合，称为无蜂窝BiBC或分布式接入点（AP）辅助BiBC，该技术可推动未来基于能量采集（EH）的物联网（IoT）网络中的潜在应用。为此，我们首先提出一种基于导频的信道估计方案，用于估计所提系统架构中的直连链路、级联链路与前向链路信道。继而利用信道估计结果，设计AP处的最优波束赋形权重、标签处的反射系数以及阅读器处的接收滤波器，以在满足标签最小能量需求的同时最大化标签总速率。由于所提最大化问题具有非凸性，我们提出了一种基于交替优化、分式规划与瑞利商技术的求解方案，并量化了所开发算法的计算复杂度。最后，通过大量数值结果验证了所提信道估计方案与优化框架的有效性，以及两种技术融合的性能。与随机波束赋形/合并基准相比，本算法取得了显著增益。例如，在10 dBm发射功率、36个AP与3个标签的场景下，所提算法在采集能量与标签总速率上分别实现了约64.8%和253.5%的增益。