Resource Allocation For Reconfigurable Intelligent Surface Assisted Dual Connectivity

<p>The next generation 6G wireless systems are envisioned to have higher reliability and capacity than the existing cellular systems. The reconfigurable intelligent surfaces (RISs) is one of the promising solution to control the wireless channel by altering the electromagnetic properties of the signal. The dual connectivity (DC) increases the per-user throughput by utilizing radio resources from two different base stations. In this work, we propose the RIS assisted DC system to improve the per-user throughput of the users by utilizing resources from two base stations (BSs) in proximity via different RISs. Given an fairness based utility function, the joint resource allocation and the user scheduling of RIS assisted DC system is formulated as an optimization problem and the optimal user scheduling time fraction is derived. The heuristic is proposed to solve the formulated optimization problem with the derived optimal scheduling time fractions. The exhaustive simulation results for coverage and throughput of the RIS assisted DC system are presented with varying user, BS, blockage, and RIS densities for different fairness values. Further, we show that the proposed RIS assisted DC system provides significant throughput gain of 52% and 48% in certain scenarios when compared to the existing benchmark and DC systems.</p>

Resource Allocation For Reconfigurable Intelligent Surface Assisted Dual Connectivity

<p>The next generation 6G wireless systems are envisioned to have higher reliability and capacity than the existing cellular systems. The reconfigurable intelligent surfaces (RISs) is one of the promising solution to control the wireless channel by altering the electromagnetic properties of the signal. The dual connectivity (DC) increases the per-user throughput by utilizing radio resources from two different base stations. In this work, we propose the RIS assisted DC system to improve the per-user throughput of the users by utilizing resources from two base stations (BSs) in proximity via different RISs. Given an fairness based utility function, the joint resource allocation and the user scheduling of RIS assisted DC system is formulated as an optimization problem and the optimal user scheduling time fraction is derived. The heuristic is proposed to solve the formulated optimization problem with the derived optimal scheduling time fractions. The exhaustive simulation results for coverage and throughput of the RIS assisted DC system are presented with varying user, BS, blockage, and RIS densities for different fairness values. Further, we show that the proposed RIS assisted DC system provides significant throughput gain of 52% and 48% in certain scenarios when compared to the existing benchmark and DC systems.</p>

Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios

The location of user equipments (UEs) allows application developers to customize the services for users to perceive an enhanced experience. In addition, this UE location enables network operators to develop location-aware solutions to optimize network resource management. Moreover, the combination of location-aware approaches and new network features introduced by 5G enables to further improve the network performance. In this sense, dual connectivity (DC) allows users to simultaneously communicate with two nodes. The basic strategy proposed by 3GPP to select these nodes is based only on the power received by the users. However, the network performance could be enhanced if an alternative methodology is proposed to make this decision. This paper proposes, instead of power-based selection, to choose the nodes that provide the highest quality of experience (QoE) to the user. With this purpose, the proposed system uses the UE location as well as multiple network metrics as inputs. A dense urban scenario is assumed to test the solution in a system-level simulation tool. The results show that the optimal selection varies depending on the UE location, as well as the increase in the QoE perceived by users of different services.