  Low-power ambient backscatter communication (AmBC) relying on radio-frequency
(RF) energy harvesting is an energy-efficient solution for batteryless Internet
of things (IoT). However, ambient backscatter signals are severely faded by
dyadic backscatter channel (DBC), limiting connectivity in conventional
orthogonal time-division-based AmBC (TD-AmBC). In order to support massive
connectivity in AmBC, we propose sparse-coded AmBC (SC-AmBC) based on
non-orthogonal signaling. Sparse code utilizes inherent sparsity of AmBC where
power supplies of RF tags rely on ambient RF energy harvesting. Consequently,
sparse-coded backscatter modulation algorithm (SC-BMA) can enable
non-orthogonal multiple access (NOMA) as well as M-ary modulation for
concurrent backscatter transmissions, providing additional diversity gain.
These sparse codewords from multiple tags can be efficiently detected at access
point (AP) using iterative message passing algorithm (MPA). To overcome DBC
along with intersymbol interference (ISI), we propose dyadic channel estimation
algorithm (D-CEA) and dyadic MPA (D-MPA) exploiting weighted-sum of the ISI for
information exchange in factor graph. Simulation results validate the potential
of the SC-AmBC in terms of connectivity, detection performance and sum
throughput.
