A multi-criteria BS switching-off algorithm for 5G heterogeneous cellular networks with hybrid energy sources

International audienceIn this paper, we study Base Station (BS) switching-off and offloading for next generation 5G heterogeneous (macro/femto) networks supplied with hybrid energy sources. This type of network will form the basis of the high-data rate energy- efficient cellular networks in the years to come. A novel generalized multimetric algorithm is presented. Our proposal is conceived to operate in highly heterogeneous Radio Access Network (RAN) environments, as expected for 5G, where BSs with different characteristics of coverage, radio resources and power consumption coexist. The approach uses a set of metrics with a modifiable priority hierarchy in order to filter, sort and select the BS neighbors, which receive traffic during redistribution and offloading of the BSs to be put into sleep mode. In our analysis, we study the impact of BS power model trends for active, idle and sleep modes on the BS switching-off. We highlight how the continuous evolution of BS components and the introduction of renewable energy technologies play a significant role to be considered in the decision making. The multimetric approach proposed makes it possible to define and accomplish defined network performance goals by adding specific emphasis on aspects like QoS, energy savings or green equipment utilization

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

Leveraging intelligence from network CDR data for interference aware energy consumption minimization

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo

Generalized Area Spectral Efficiency: An Effective Performance Metric for Green Wireless Communications

Area spectral efficiency (ASE) was introduced as a metric to quantify the spectral utilization efficiency of cellular systems. Unlike other performance metrics, ASE takes into account the spatial property of cellular systems. In this paper, we generalize the concept of ASE to study arbitrary wireless transmissions. Specifically, we introduce the notion of affected area to characterize the spatial property of arbitrary wireless transmissions. Based on the definition of affected area, we define the performance metric, generalized area spectral efficiency (GASE), to quantify the spatial spectral utilization efficiency as well as the greenness of wireless transmissions. After illustrating its evaluation for point-to-point transmission, we analyze the GASE performance of several different transmission scenarios, including dual-hop relay transmission, three-node cooperative relay transmission and underlay cognitive radio transmission. We derive closed-form expressions for the GASE metric of each transmission scenario under Rayleigh fading environment whenever possible. Through mathematical analysis and numerical examples, we show that the GASE metric provides a new perspective on the design and optimization of wireless transmissions, especially on the transmitting power selection. We also show that introducing relay nodes can greatly improve the spatial utilization efficiency of wireless systems. We illustrate that the GASE metric can help optimize the deployment of underlay cognitive radio systems.Comment: 11 pages, 8 figures, accepted by TCo