Visible to the public Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing

TitleReinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing
Publication TypeConference Paper
Year of Publication2020
AuthorsShi, Y., Sagduyu, Y. E., Erpek, T.
Conference Name2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
Keywords5G mobile communication, 5G radio access network slicing, 5G security, Bit error rate, composability, computational constraints, dynamic networks, dynamic resource allocation, dynamic resource optimization, Dynamic scheduling, frequency-time blocks, learning (artificial intelligence), Metrics, network optimization, network slice requests, network slicing, Optimization, pubcrawl, Q-learning solution, Radio Access Network, radio access networks, reinforcement learning, resilience, Resiliency, resource allocation, Resource management, telecommunication computing, Throughput
AbstractThe paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks and transmit powers) and computational resources (processor usage) are allocated to stochastic arrivals of network slice requests. Each request arrives with priority (weight), throughput, computational resource, and latency (deadline) requirements, and if feasible, it is served with available communication and computational resources allocated over its requested duration. As each decision of resource allocation makes some of the resources temporarily unavailable for future, the myopic solution that can optimize only the current resource allocation becomes ineffective for network slicing. Therefore, a Q-learning solution is presented to maximize the network utility in terms of the total weight of granted network slicing requests over a time horizon subject to communication and computational constraints. Results show that reinforcement learning provides major improvements in the 5G network utility relative to myopic, random, and first come first served solutions. While reinforcement learning sustains scalable performance as the number of served users increases, it can also be effectively used to assign resources to network slices when 5G needs to share the spectrum with incumbent users that may dynamically occupy some of the frequency-time blocks.
Citation Keyshi_reinforcement_2020