User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks

Abstract

The last decades have been characterized by an exponential increase in digital services. The demand is foreseen to further increase in the next years, and mobile networks will have to mandatorily supply connections to enable digital services with very different requirements, from ultra high speed to ultra low latency. The deployment and the coexistence of cells of different size, from femto to macro, will be one of the key elements for providing such pervasive wireless connection: the ultra dense networks (UDN) paradigm. How to associate users and base stations is one of the most investigated research topics. Many criteria can be drawn, from minimization of power consumption to optimization of throughput. In this paper we propose a new utility to optimize two of the most important features of future mobile connection: security and energy consumption. By using our utility it is possible to jointly select the base station to be activated in a UDN, and associate users to the base stations with the aim of maximizing the secure throughput by spending the minimum energy. Moreover, we propose a heuristic that allows to achieve performance very close to the optimal one with reduced complexity. Effectiveness of the proposed approach is proved by means of comparison with benchmark approaches.

Joint user association and energy efficiency maximization in beyond 5G heterogeneous networks

“…18 User association and resource allocation optimization problem using deep learning algorithm in HetNets is investigated in Ding et al 19 Energy efficiency along with secure throughput objective is investigated by associating the user with a suitable BS using the heuristic algorithm by authors. 20 Joint user association and power allocation to maximize energy efficiency in HetNets employing deep reinforcement learning (DRL)-based approach has been studied in Hsieh et al 21 Solutions based on cloud services are adopting management systems with maximum EE to minimize energy consumption. A model has been purposed for Virtual Machines (VMs) to manage operations such as their placement, start-up, and migration.…”

“…After looking at the Table 1 and going through past literature [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] to the best of the author's knowledge, it is concluded that joint users association and EE maximization for effective radio resource management in HetNets with macro and small cells assisted by relays and D2D has not been explored in the past. The main contributions of the proposed work are summarized below:…”

“…After looking at the Table 1 and going through past literature 14‐32 to the best of the author's knowledge, it is concluded that joint users association and EE maximization for effective radio resource management in HetNets with macro and small cells assisted by relays and D2D has not been explored in the past. The main contributions of the proposed work are summarized below: We give mathematical formulation for the joint user and EE maximization in HetNets with macro and small cell assisted by relay and D2D. Formulated problem is concave fractional programming (CFP) and is converted into a concave optimization problem with the help of Charnes‐Cooper Transformation. Mesh Adaptive Direct Search (MADS) algorithm has been used to get optimized solution of formulated problem. Results can be verified from the comparison between MADS and outer approximation algorithm (OAA) where the solution of both algorithms confirms the superiority of the MADS algorithm. …”

Evaluation of the effect of 5G Hardware Impairments on achieving high Energy Efficiency

“…Various techniques for power efficient community layout for wireless mobile network operations have been presented in the literature. In [3] grouped EE approaches into five categories, the majority of which coincide with those defined in [4] and [6]. These techniques are categorized into the five groups stated below.…”

Distributed Joint Optimization of Beamforming and Power Allocation for Maximizing the Energy Efficiency of Cognitive Heterogeneous Networks

“…In this context, there have been attempts to develop energy-efficient communications for CHNs [ 11 , 12 , 13 , 14 ]. In [ 11 , 12 ], resource allocations were investigated to maximize the energy efficiency of underlying device-to-device (D2D) communications, and, in [ 13 , 14 ], energy-efficient user-association methods were also investigated. To further improve the energy efficiency of CHNs, studies on multi-antenna techniques have been undertaken [ 15 , 16 , 17 , 18 ].…”