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In order to fully explore the merits of the multi-tier heterogeneous network (HetNet) and ultra-dense small cell deployments, the evolution of the cellular network architecture is on the way. Based on the frameless network architecture, this paper proposes the control plane (CP) and user plane (UP) adaptation strategy to improve the system energy efficiency (EE). In this paper, we give a three-step system EE optimization with constraints on the CP/UP adaptation. We optimize the system EE via CP and UP construction and adaptation while guaranteeing the user quality of service (QoS). In the first step, the CP construction and adaptation scheme is proposed through the Voronoi Diagram with EE improvements. In the second step, by jointly allocating the AE and subchannel resources, we construct the on-demand user-centric UP. In the last step, based on the UP construction from the second step, we adjust each UP to optimize the EE performance. This is achieved by employing game theory. Furthermore, we verify the existence and uniqueness of the Nash Equilibrium for the non-cooperative game model with pricing. Finally, the system-level simulation results show that, with constraints on the QoS of the users, the system EE performances are improved. [ABSTRACT FROM AUTHOR]

In this paper, multiple device-to-device (D2D) communication underlaying cellular multiuser multiple inputs multiple outputs (MU-MIMO) systems is investigated. This type of communication can improve spectral efficiency to address future demand, but interference management, user clustering, and resource allocation are three key problems related to resource sharing. Interference alignment (IA) is proposed to better mitigate in-cluster interference compared with a multiplex scheme, and user clustering and resource allocation are jointly investigated using binary-integer programming. In addition to an exhaustive search for a maximum throughput, we propose a two-step suboptimal algorithm by reducing the search space and applying branch-and-bound searching (BBS). To further obtain a good trade-off between performance and complexity, we propose a novel algorithm based on distance-constrained criteria for user clustering. The simulation results show that the IA and multiplex schemes acquiring user clustering gains outperform the orthogonal scheme without user clustering. Besides, the proposed two-step and location-based algorithms achieve little losses compared with the optimal algorithm under low complexities. [ABSTRACT FROM AUTHOR]