A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks

Cell Switch-Off (CSO) is recognized as a promising approach to reduce the energy consumption in next-generation cellular networks. However, CSO poses serious challenges not only from the resource allocation perspective but also from the implementation point of view. Indeed, CSO represents a difficult optimization problem due to its NP-complete nature. Moreover, there are a number of important practical limitations in the implementation of CSO schemes, such as the need for minimizing the real-time complexity and the number of on-off/off-on transitions and CSO-induced handovers. This article introduces a novel approach to CSO based on multiobjective optimization that makes use of the statistical description of the service demand (known by operators). In addition, downlink and uplink coverage criteria are included and a comparative analysis between different models to characterize intercell interference is also presented to shed light on their impact on CSO. The framework distinguishes itself from other proposals in two ways: 1) The number of on-off/off-on transitions as well as handovers are minimized, and 2) the computationally-heavy part of the algorithm is executed offline, which makes its implementation feasible. The results show that the proposed scheme achieves substantial energy savings in small cell deployments where service demand is not uniformly distributed, without compromising the Quality-of-Service (QoS) or requiring heavy real-time processing

A Planning and Optimization Framework for Hybrid Ultra-Dense Network Topologies

The deployment of small cells has been a critical upgrade in Fourth Generation (4G) mobile networks as they provide macrocell traffic offloading gains, improved spectrum reuse and reduce coverage holes. The need for small cells will be even more critical in Fifth Generation (5G) networks due to the introduction of higher spectrum bands, which necessitate denser network deployments to support larger traffic volumes per unit area. A network densification scenario envisioned for evolved fourth and fifth generation networks is the deployment of Ultra-Dense Networks (UDNs) with small cell site densities exceeding 90 sites/km2 (or inter-site distances of less than 112 m). The careful planning and optimization of ultra-dense networks topologies have been known to significantly improve the achievable performance compared to completely random (unplanned) ultra-dense network deployments by various third-part stakeholders (e.g. home owners). However, these well-planned and optimized ultra-dense network deployments are difficult to realize in practice due to various constraints, such as limited or no access to preferred optimum small cell site locations in a given service area. The hybrid ultra-dense network topologies provide an interesting trade-off, whereby, an ultra-dense network may constitute a combination of operator optimized small cell deployments that are complemented by random small cell deployments by third-parties. In this study, an ultra-dense network multiobjective optimization framework and post-deployment power optimization approach are developed for realization and performance comparison of random, optimized and hybrid ultra-dense network topologies in a realistic urban case study area. The results of the case study demonstrate how simple transmit power optimization enable hybrid ultra-dense network topologies to achieve performance almost comparable to optimized topologies whilst also providing the convenience benefits of random small cell deployments

Designing problem-specific operators for solving the Cell Switch-Off problem in ultra-dense 5G networks with hybrid MOEAs

The massive deployment of base stations is one of the key pillars of the fifth generation (5G) of mobile communications. However, this network densification entails high energy consumption that must be addressed to enhance the sustainability of this industry. This work faces this problem from a multi-objective optimization perspective, in which both energy efficiency and quality of service criteria are taken into account. To do so, several newly problem-specific operators have been designed so as to engineer hybrid multi-objective evolutionary metaheuristics (MOEAs) that bring expert knowledge of the domain to the search of the algorithms. These hybrid approaches have been able to improve upon canonical versions of the algorithms, clearly showing the contributions of our approach. Furthermore, this paper tests the hypothesis that the hybridization using several of those problem-specific operators simultaneously can enhance the search of MOEAs that are endowed only with a single one.Funding for open access charge: Universidad de Málaga / CBUA This work has been partially funded by the Spanish Ministry of Science and Innovation via grant PID2020-112545RB-C54, by the European Union NextGenerationEU/PRTR under grants TED2021-131699B-I00 and TED2021-129938B-I00 (MCIN/AEI/10.13039/501100011033, FEDER) and the Andalusian PAIDI program with grants A-TIC-608-UGR20, P18.RT.4830, and PYC20-RE-012-UGR. The authors also thank the Supercomputing and Bioinformatics Center of the Universidad de Málaga, for providing its services and the Picasso supercomputer facilities to perform the experiments (http://www.scbi.uma.es/). Funding for open access charge: Universidad de Málaga/CBUA

Spectral-Energy Efficiency Trade-off-based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems

Energy efficiency (EE) and spectral efficiency (SE) are two of the key performance metrics in future wireless networks, covering both design and operational requirements. For previous conventional resource allocation techniques, these two performance metrics have been considered in isolation, resulting in severe performance degradation in either of these metrics. Motivated by this problem, in this paper, we propose a novel beamforming design that jointly considers the trade-off between the two performance metrics in a multiple-input single-output non-orthogonal multiple access system. In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good tradeoff between the two performance metrics. However, this MOO problem is not mathematically tractable and, thus, it is difficult to determine a feasible solution due to the conflicting objectives, where both need to be simultaneously optimized. To overcome this issue, we exploit a priori articulation scheme combined with the weighted sum approach. Using this, we reformulate the original MOO problem as a conventional single objective optimization (SOO) problem. In doing so, we develop an iterative algorithm to solve this non-convex SOO problem using the sequential convex approximation technique. Simulation results are provided to demonstrate the advantages and effectiveness of the proposed approach over the available beamforming designs.Comment: Accepted in IEEE TWC, June 202

Reconfigurable Intelligent Surface-Assisted B5G/6G Wireless Communications: Challenges, Solution and Future Opportunities

The power consumption and hardware cost are two of the main challenges for realizing beyond fifth-generation (B5G) and sixth-generation (6G) wireless communications. Recently, the emerging reconfigurable intelligent surface (RIS) have been recognized as a promising tool for enhancing the propagation environment and improving the spectral efficiency of wireless communications by controlling low-cost passive reflecting elements. However, current cellular communication were designed on the basis of conventional communication theories, significantly restrict the development of RIS-assisted B5G/6G technologies and lead to severe limitations. In this article, we discuss RIS-assisted channel estimation issues involved in B5G/6G communications including channel state information (CSI) acquisition, imperfect cascade CSI for beamforming design and co-channel interference coordination, and develop a few possible solutions or visionary technologies to promote the development of B5G/6G. Finally, potential research opportunities are discussed