Resource allocation in parasitic symbiotic radio networks with hybrid long and short-packet transmissions

This work investigates the resource allocation for a parasitic symbiotic radio (PSR) network, in which a primary transmitter performs long-packet communication while multiple Internet of Things (IoT) nodes sequentially backscatter their short-packet data to a cooperative receiver. To enhance the primary performance, we propose a novel transmission framework that enables each IoT node not only to backscatter its own short-packet data, but also to assist the primary transmission by reflecting the primary signal without modulating its own information. Within this framework, we formulate an optimization problem to maximize the primary achievable throughput under reliability constraints for all IoT nodes, which yields a highly non-convex mixed-integer nonlinear programming problem. To solve it efficiently, we first employ the block coordinate descent (BCD) method to decouple the problem into two subproblems: the first is solved optimally via the bisection method, and the second is proved to be convex and handled with standard convex tools. A BCD-based iterative algorithm is then developed, which alternately updates the two subproblems until convergence. Simulation results verify the fast convergence of the proposed algorithm and demonstrate that, compared with the existing scheme, the proposed scheme achieves higher primary throughput while satisfying each IoT node’s reliability requirement.