Optimizing Passive Tag Performance With Reconfigurable Intelligent Surfaces in Bistatic Backscatter Networks

Passive tags in bistatic backscatter networks have a myriad of industry applications. However, due to insufficient energy harvesting (EH), tags have poor communication ranges, activation distances, and data rates. To overcome these challenges, we employ a reconfigurable intelligent surface (RIS) to enhance the incident radio frequency power at the tag. We consider linear and non-linear EH models and analyze single-tag and multi-tag scenarios. For single-tag networks, we optimize the RIS phase shifts to maximize the incident power at the tag and the signal-to-noise ratio of the reader. Key metrics, such as received power, harvested power, achievable rate, outage probability, bit error rate, and diversity order, are also derived. The impact of RIS phase shift quantization errors is also studied. For the multi-tag case, an algorithm to compute the optimal RIS phase shifts is developed. Numerical results and simulations demonstrate significant improvements compared to the benchmarks of no-RIS case and random RIS-phase design. For instance, our optimal design with a 200-element RIS increases the activation distance of tags by 270% and 55% compared to those benchmarks. In summary, the proposed RIS solution improves the energy autonomy of tags while keeping the basic tag design intact.