Computational Intelligence Techniques for Mobile Network Optimization [Guest Editorial]

Modern society has become increasingly reliant on mobile networks for their communication needs. Such networks are characterized by their dynamic, heterogeneous, complex, and data intensive nature, which makes them more amenable to automated mobile network optimization performed using “computational intelligence’’ (CI) techniques rather than traditional optimization approaches. CI techniques—which subsume multidisciplinary techniques from machine learning (ML), optimization theory, game theory, control theory, and meta-heuristics— have a rich history in terms of being deployed in networking. CI techniques are highly suited to the mobile networking architectures and the dynamic environments they characterize. Looking ahead, it looks likely that CI will play a leading role in upcoming 5th generation (5G) wireless mobile networks for developing optimized solutions for vexing problems—such as traffic scheduling and routing, capacity, coverage, and power optimization—in the face of stringent requirements and highly dynamic conditions. The importance of our proposed theme of mobile network optimization (MNO) motivated us to propose this special issue in the IEEE Computational Intelligence Magazine (CIM)—the premier IEEE magazine for professionals interested in CI techniques and their applications.