Toward Structural Sparse Precoding: Dynamic Time, Frequency, Space, and Power Multistage Resource Programming

In the last decades, dynamic resource programming in partial resource domains has been extensively investigated for single-time-slot optimizations. However, with the emerging real-time media applications in sixth-generation (6G) communications, their new quality of service requirements is often measured in the temporal dimension. This requires multistage optimization for full resource domain dynamic programming. Taking experience rate as a typical temporal metric, we jointly optimize time, frequency, space, and power domains’ resources for multistage optimization. To strike a good tradeoff between system performance and computational complexity, we first transform the formulated mixed integer nonlinear constraints into equivalent convex second-order cone (SOC) constraints by exploiting the coupling effect among the resources. Leveraging the concept of structural sparsity, the objective of the max–min experience rate is given as a weighted one-norm term associated with the precoding matrix. Finally, a low-complexity iterative algorithm is proposed for full resource domain programming, aided by another simple conic optimization for obtaining its feasible initial result. Simulation verifies that our design significantly outperforms the benchmarks while maintaining a fast convergence rate, shedding light on full domain dynamic resource programming (FDRP) of multistage optimizations.