Research Article
Dynamically Resource Allocation in Beyond 5G (B5G) Network RAN Slicing Using Deep Deterministic Policy Gradient
Table 2
RL-based resource allocation.
| Ref. | Algorithm | Focus | Optimization objective | Use case/vertical app | Training | Development |
| [17] | DQN | RAN | Improve resource consumption and slice isolation | Continuous bit rate and lowest bit rate | Centralized | Simulation | [18] | -learning | | Maximization of resource use while assembling the elements of successful communication | Haptic | Centralized | Simulation | [19] | -learning, SARSA, and Monte Carlo | RAN | Assurance of efficient resource use while fulfilling the demands for low latency | Internet of Things | Centralized | Simulation | [20, 21] | DDQN and duelling DQN | RAN | Maximize long-term profits while offering the services that different multitenant customers require | Manufacturing, automotive, and utilities | Centralized | Emulation (TensorFlow) | [22] | DQN | RAN | Maximize the utilisation of radio resources while preserving QoS | eMBB, mIoT, and uRLLC | Centralized | Simulation | [23] | DQN | E2E (RAN, TN, CN, edge) | SFC traffic variations should be accommodated when VNF placement is optimised | eMBB | Centralized | Emulation (OpenAI gym) | [24, 25] | LSTM | RAN | It is necessary to maximize spectrum efficiency and the SLA satisfaction ratio | VoLTE, eMBB, and uRLLC | Centralized | Simulation | [8] | A3C | RAN | Making the most of resources while preserving slice separation | Undeclared | Distributed | Emulation (TensorFlow) |
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