Globally Optimal Resource Allocation and Time Scheduling in Downlink Cognitive CRAN Favoring Big Data Requests

Abstract

This paper is concerned with a cognitive cloud radio access network (CRAN) with a special attention to efficient and reliable downlink transmission of big data for secondary users (SUs). Existing approaches either try to maximize the number of accepted SUs or the sum data rate of accepted SUs. The first approach unfairly favors users with small data requests, whereas the second approach allocates most resources to users with better channel conditions. In contrast, this paper develops a novel approach that favors big data requests while simultaneously maintaining a certain degree of fairness among SUs. To this end, we first introduce a novel objective function that allows us to jointly optimize deadline-aware time scheduling, spectrum allocation, SU selection, and remote radio head (RRH) allocation for SUs. Second, we demonstrate that finding the global optimum solution entails the enumeration of all colorful independent sets on a generalized interval graph, which is known to be NP-hard. Third, we propose a dynamic programming (DP) approach, which yields the global optimum solution at a reduced computational cost. Fourth, we analyze the complexity of the proposed DP approach and assess its performance against existing baseline algorithms. Simulation results reveal that our solution favors big data users while incurring only a small degradation in the fairness index. Our proposed solution is practical for small-to-medium size networks. Furthermore, it offers an optimum benchmark for any new sub-optimal low-complexity algorithm.