Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networks

Abstract

Dense deployment of small cells is seen as one of the major approaches for addressing the traffic demands in next-generation 5G wireless networks. The energy efficiency, however, becomes a key concern together with this massive amount of small cells. In this study, we therefore consider the energyefficient small cell networks (SCN) using smart on/off scheduling (OOS) strategies, where a certain fraction of small base stations (SBS) are put into less energy-consuming sleeping states to save energy.To this end, we first represent the overall SCN traffic by a new load variable, and analyze its statistics rigorously using Gamma approximation. We then propose two novel OOS algorithms exploiting this load variable in centralized and distributed fashions. We show that proposed load based OOS algorithms can lead to as high as 50% of energy savings without sacrificing the average SCN throughput. In addition, load based strategies are shown to work well under high SCN traffic and delay-intolerant circumstances, and can be implemented efficiently using the load statistics. We also show that the performance of load based algorithms gets maximized for certain length of sleeping periods, where assuming short sleep periods is as energy-inefficient as keeping SBSs in sleep states for very long. Index Terms5G, delay tolerant network (DTN), energy efficiency, sleep mode, small cell network (SCN). H. Ç elebi is with theMassive densification of small cell networks (SCNs) is commonly seen as one of the major pillars of 5G wireless networks to cope with the ever-increasing mobile data traffic [1], [2].For such dense deployments of SCNs, developing dynamic cell management and user-access mechanisms are crucial for saving energy at off-peak hours and for boosting the throughput of the network [3], [4]. Active cells not only consume energy, but also increase interference in the communication environment. Therefore, green and energy-efficient strategies that opportunistically place cells into sleep mode becomes important for unplanned cell locations, especially with dynamically varying user distributions, spatial load, and traffic load.Due to user mobility and varying traffic demand, number of small cells that are required to satisfy the quality of service requirements (QoS) of users change continuously. In particular, numerous types of user equipment (UEs) such as tablets, mobile phones, gaming consoles, e-readers, and machine type devices cause heterogeneous traffic patterns. This heterogeneous UE traffic environment necessitates energy-efficient and dynamic techniques where the small cell base stations (SBSs) switch to sleep mode when they are not needed, and they can also dynamically get activated when the demand is high.While there are several recent techniques in the literature for energy-efficient small cells [4]- [7], energy savings can be further improved by dynamic switching based on short-term service demand, and integrating energy-efficient sleep mode techniques with flexible access strategies for UEs. For example, in [8], the...

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“…Recently, more advanced energy management techniques have been proposed [25]- [29]. For example, in [25], Pen et.…”

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DeCoNet: Density Clustering-Based Base Station Control for Energy-Efficient Cellular IoT Networks

“…In [15], a Markov decision process (MDP)-based optimal wake-up mechanism for femtocell BSs was proposed to minimize the energy consumption of heterogeneous networks, and in [16], energy-efficient user association and power allocation methods were proposed for mmWave-based UDNs with energy-harvesting BSs. Furthermore, Z. Jian et al proposed a joint optimization framework for an energy-efficient switching on/off strategy and user association policy in dense cellular networks with partial conventional BSs in [17], and traffic load distribution-based on/off scheduling algorithms were proposed for energyefficient delay-tolerant 5G networks in [18].…”