: In order to meet demanding performance objectives in Long-Term Evolution (LTE) networks, it is mandatory to implement highly-efficient, autonomic self-optimization and configuration processes.
The configuration of a neighbour cell list (NCL) in cellular networks has an important impact on the number of dropped calls and is traditionally optimized manually with the help of planning tools.
Network optimization is used by operators to maximize return on investment and to ensure customer satisfaction with the quality of the delivered service.
In this paper, we propose a self-optimized downlink power allocation algorithm, which utilizes the concepts of game theory and fuzzy logic inference system.
In this paper, we propose a new concept of a knowledge management framework to enable a self-optimizing and self-learning for wireless system operation in real time.
In this paper, we propose an autonomous radio resource allocation and optimization scheme that chooses the transmit power and precoding vector among codebooks for multiple antennas transmitters to
As licensed spectrum is scarce and users have ever increasing throughput and data volume requirements, mobile network operators (MNOs) are looking for innovative ways for using the unlicensed spect
We describe a novel wireless backhaul solution, called Self-Optimizing Wireless Mesh Network (SWMN), which is targeted for interconnecting 5G small cell nodes cost-efficiently and smartly.
Self-organization is a key factor for the future evolution of mobile networks, especially for introduction of the new radio standard LTE.
In this paper, the problem of channel selection and power control is jointly analyzed in the context of multiple-channel clustered ad-hoc networks, i.e., decentralized networks in which radio devic
