Abstract
With the advent of network densification and the development of other radio interface technologies, the major bottleneck of future cellular networks is shifting from the radio access network to the backhaul. The future networks are expected to handle a wide range of applications and users with different requirements. In order to tackle the problem of downlink user-cell association, and allocate users to the best cell, an intelligent solution based on reinforcement learning is proposed. A distributed solution based on Q-Learning is developed in order to determine the best cell range extension offsets (CREOs) for each small cell (SC) and the best weights of each user requirement to efficiently allocate users to the most appropriate SC, based on both backhaul constraints and user demands. By optimizing both CREOs and user weights, a user-specific allocation can be achieved, resulting in a better overall quality of service. The results show that the proposed algorithm outperforms current solutions by achieving better user satisfaction, mitigating the total number of users in outage, and minimizing user dissatisfaction when satisfaction is not possible.
Highlights
T HE Generation of Mobile Networks (NGMN), 5G, is under heavy pressure in order to overcome limitations of current cellular networks, and to enable and push the boundaries of future networks to a level
By optimizing both network and user parameters, the proposed solution is able to allocate to each user what it needs without wasting network resources and making other users suffer, this, in its turn, enables more users to be allocated to the network while satisfying their needs, improving individual and overall Quality of Service (QoS)
It is considered that each user has μ = 3 requirements based on throughput, latency and resilience
Summary
T HE Generation of Mobile Networks (NGMN), 5G, is under heavy pressure in order to overcome limitations of current cellular networks, and to enable and push the boundaries of future networks to a level. A user-specific cell association algorithm is proposed in order to tackle the problem of allocating users with distinct requirements to the best fitting Small Cells (SCs) with different backhaul parameters. The main idea and innovation behind the proposed Reinforcement Learning (RL) based algorithm is, to perform two different optimizations, one at the network level, in which the algorithm optimizes the CREO of SCs via Q-Learning, followed by another optimization at the user level, in which the algorithm determines the best weights for each user, via Q-Learning Combining both Q-Learning solutions and optimizing both network and user parameters, the proposed solution is able to provide user-specific allocation and achieve better results in terms of user satisfaction and QoS
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