  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 non-convex 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. Simulation results verify the effectiveness of the proposed algorithm by
numerical examples in terms of the achieved system throughput.
