scholarly journals Placement Optimization of Aerial Base Stations with Deep Reinforcement Learning

2020 ◽  
Author(s):  
Jin Qiu ◽  
Jiangbin Lyu ◽  
Liqun Fu
Keyword(s):  
Reinforcement Learning ◽  
Base Stations ◽  
Placement Optimization

scholarly journals A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

2021 ◽  
Vol 15 (12) ◽  
pp. 65
Author(s):  
Akindele Segun Afolabi ◽  
Shehu Ahmed ◽  
Olubunmi Adewale Akinola
Keyword(s):  
Reinforcement Learning ◽  
Power Level ◽  
Heterogeneous Wireless Networks ◽  
Interference Management ◽  
Base Station ◽  
User Equipment ◽  
Base Stations ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Macro Cell

<span lang="EN-US">Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. This paper developed a reinforcement learning model that assisted in coordinating interference in a heterogeneous network comprising macro-cell and pico-cell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the proposed Q-learning scheme improved the performances of average user equipment throughput in the network. In particular, </span><span lang="EN-US">multi-agent systems with a normal learning rate increased the throughput of associated user equipment by a whooping 212.5% compared to a macrocell-only scheme.</span>

Placement Optimization of Unmanned Aerial Vehicle Base Stations for Maximal Coverage

2021 ◽  
Author(s):  
Yanzhi Hu ◽  
Fengbin Zhang ◽  
Tian Tian ◽  
Zhiyong Shi ◽  
Gang Yu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Base Stations ◽  
Placement Optimization ◽  
Aerial Vehicle

scholarly journals Dynamic handoff policy for RAN slicing by exploiting deep reinforcement learning

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yuansheng Wu ◽  
Guanqun Zhao ◽  
Dadong Ni ◽  
Junyi Du
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Resource Constraints ◽  
Base Stations ◽  
Service Needs ◽  
Network Slicing ◽  
Logical Connection ◽  
Radio Access ◽  
Markov Decision ◽  
Underlying Network

AbstractIt has been widely acknowledged that network slicing is a key architectural technology to accommodate diversified services for the next generation network (5G). By partitioning the underlying network into multiple dedicated logical networks, 5G can support a variety of extreme business service needs. As network slicing is implemented in radio access networks (RAN), user handoff becomes much more complicated than that in traditional mobile networks. As both physical resource constraints of base stations and logical connection constraints of network slices should be considered in handoff decision, an intelligent handoff policy becomes imperative. In this paper, we model the handoff in RAN slicing as a Markov decision process and resort to deep reinforcement learning to pursue long-term performance improvement in terms of user quality of service and network throughput. The effectiveness of our proposed handoff policy is validated via simulation experiments.

scholarly journals Deep Reinforcement Learning for the Computation Offloading in MIMO-based Edge Computing

2021 ◽  
Author(s):  
Abdeladim Sadiki ◽  
Jamal Bentahar ◽  
Rachida Dssouli ◽  
Abdeslam En-Nouaary
Keyword(s):  
Reinforcement Learning ◽  
High Performance ◽  
Multiple Input Multiple Output ◽  
Edge Computing ◽  
Base Stations ◽  
Computation Offloading ◽  
Discrete Action ◽  
Computationally Intensive ◽  
Multi Access ◽  
Policy Optimization

Multi-access Edge Computing (MEC) has recently emerged as a potential technology to serve the needs of mobile devices (MDs) in 5G and 6G cellular networks. By offloading tasks to high-performance servers installed at the edge of the wireless networks, resource-limited MDs can cope with the proliferation of the recent computationally-intensive applications. In this paper, we study the computation offloading problem in a massive multiple-input multiple-output (MIMO)-based MEC system where the base stations are equipped with a large number of antennas. Our objective is to minimize the power consumption and offloading delay at the MDs under the stochastic system environment. To this end, we formulate the problem as a Markov Decision Process (MDP) and propose two Deep Reinforcement Learning (DRL) strategies to learn the optimal offloading policy without any prior knowledge of the environment dynamics. First, a Deep Q-Network (DQN) strategy to solve the curse of the state space explosion is analyzed. Then, a more general Proximal Policy Optimization (PPO) strategy to solve the problem of discrete action space is introduced. Simulation results show that the proposed DRL-based strategies outperform the baseline and state-of-the-art algorithms. Moreover, our PPO algorithm exhibits stable performance and efficient offloading results compared to the benchmark DQN strategy.

scholarly journals Dynamic Handoff Policy for RAN Slicing by Exploiting Deep Reinforcement Learning

2021 ◽  
Author(s):  
Yuansheng Wu ◽  
Guanqun Zhao ◽  
Dadong Ni ◽  
Junyi Du
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Resource Constraints ◽  
Base Stations ◽  
Service Needs ◽  
Network Slicing ◽  
Logical Connection ◽  
Radio Access ◽  
Markov Decision ◽  
Underlying Network

Abstract It has been widely acknowledged that network slicing is a key architectural technology to accommodate diversified services for the next generation network (5G). By partitioning the underlying network into multiple dedicated logical networks, 5G can support a variety of extreme business service needs. As network slicing is implemented in radio access networks (RAN), user handoff becomes much more complicated than that in traditional mobile networks. As both physical resource constraints of base stations (BSs) and logical connection constraints of network slices should be considered in handoff decision, an intelligent handoff policy becomes imperative. In this paper, we model the handoff in RAN slicing as a Markov decision process (MDP) and resort to deep reinforcement learning to pursue long-term performance improvement in terms of user quality of Service (QoS) and network throughput. The effectiveness of our proposed handoff policy is validated via simulation experiments.

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