Efficient resource allocation and interference management using compressive sensing in dense mobile communication systems

In dense mobile communication systems, time efficiency is a vital demand for resource allocation (RA), as well as inter-cell interference coordination (ICIC), due to the fact that the scale of RA optimization is extremely large. Existing optimization schemes, such as Hungarian algorithm, are too time-consuming to reach an optimal solution, which does not fit for RA in dense scenarios. In this paper, we employ the compressive sensing (CS) technique to design a CS-based scheme, which uses wavelet transform, Hadamard matrix, and CoSaMP algorithm for sparse representation, measurement, and reconstruction, respectively. Simulation results show the sparsity, the feasibility, and the gain of time efficiency by using the CS technique. We can see that, in dense system, our proposal could achieve a near-optimal RA solution with significantly decreased time cost. Meanwhile, in 2-cell scenario, the interference can also be coordinated effectively by the CS-based RA scheme.