15 research outputs found
Reinforcement Learning Based Handoff for Millimeter Wave Heterogeneous Cellular Networks
The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named LESH to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In LESH, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, LESH can significantly reduce the number of handoffs when compared with traditional handoff policies
The SMART handoff policy for millimeter wave heterogeneous cellular networks
The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets that brings heavy signaling overhead, low energy efficiency and increased user equipment (UE) outage probability if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named SMART to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In SMART, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, SMART can significantly reduce the number of handoffs when compared with traditional handoff policies without learning
6G Network AI Architecture for Everyone-Centric Customized Services
Mobile communication standards were developed for enhancing transmission and
network performance by using more radio resources and improving spectrum and
energy efficiency. How to effectively address diverse user requirements and
guarantee everyone's Quality of Experience (QoE) remains an open problem. The
Sixth Generation (6G) mobile systems will solve this problem by utilizing
heterogenous network resources and pervasive intelligence to support
everyone-centric customized services anywhere and anytime. In this article, we
first coin the concept of Service Requirement Zone (SRZ) on the user side to
characterize and visualize the integrated service requirements and preferences
of specific tasks of individual users. On the system side, we further introduce
the concept of User Satisfaction Ratio (USR) to evaluate the system's overall
service ability of satisfying a variety of tasks with different SRZs. Then, we
propose a network Artificial Intelligence (AI) architecture with integrated
network resources and pervasive AI capabilities for supporting customized
services with guaranteed QoEs. Finally, extensive simulations show that the
proposed network AI architecture can consistently offer a higher USR
performance than the cloud AI and edge AI architectures with respect to
different task scheduling algorithms, random service requirements, and dynamic
network conditions
Efficient multicast routing with delay constraints
To support real-time multimedia applications in BISDN networks, QoS guaranteed multicast routing is essential. Traditional multicast routing algorithms used for solving the Steiner tree problem cannot be used in this scenario, because QoS constraints on links are not considered. In this paper, we present two efficient source-based multicast routing algorithms in directed networks. The objective of the routing algorithms is to minimize the multicast tree cost while maintaining a bound on delay. Simulation results show that these two heuristics can greatly improve the multicast tree cost measure in comparison with the shortest path routing schemes. Their performance is close to that of the known CST � algorithm proposed by Kompell et al. in Reference 1, but requiring a much shorter computation time