## 2 Literature Review
Dynamic sleep mode scheme performs better than fixed schemes but due to an increase in the number of switching operations, its computation complexities are also higher [32]. The dynamic scheme is further divided into two subcategories named, centralized schemes (CS) and distributed schemes (DS). DS approach provides more ease in terms of its implementation and manageability but has some trade-off in terms of its performance as CS have knowledge of all BS in the entire network, so their impacts are high when compared with distributed ones. Moreover, introducing sleep mode in small cells can minimize the overall consumption when compared to a macro-only network [33]. However, sleeping mode also introduced some fundamental limitations on some important factors like optimum QoS, outage, coverage, and throughput. Consequently, only certain privileged users of macro-cell cancer can have a high data rate, while others may suffer from outages. In [34], optimized UA was used through total data rate and energy consumption ratio maximization. However, this approach does not provide an improvement in the energy efficiency of the network and ignores the negative impact on QoS parameters [2]. Figure 4: Sleep enabling techniques. Numerous valuable contributions have been published on energy efficiency for HetNets by considering the few QoS parameters and the effect of traffic variations [35; 36]. Nevertheless, the common problem is that they have considered the traditional homogeneous hexagonal grid (HHG) model for the network topology design. The authors in [37; 38; 39; 40; 41] have to use stochastic geometry for performance analysis of HetNets. In particular, the authors in [37; 38] have designed a k-tier HetNet using stochastic geometry and defined outage probabilities and average data throughput for the multi-tier network. State-of-the-Art Sleep Mode StrategiesRandom sleep mode (RSM) and load-aware sleep mode (LSM) currently represent the state-of-the-art BS switch-off strategies within contemporary HetNets [22; 28; 30; 42], as shown in Figure 5. However, they come up with their own limitations and challenges. The most straightforward method for switching off small BSs involves independently turning them on or off based on a specified probability. The authors in [43; 44] have explored algorithms based on random sleep mode (RSM). These schemes utilize probabilistic models such as the Bernoulli processes [45], the exponential process [46], the uniform process [47], the Poisson process [48], etc., which have been extensively investigated. Moreover, in practical networks, data traffic loads vary across different geographical areas and are not uniform. These fluctuations in non-uniform traffic loads present an opportunity to reduce energy consumption by powering down less-utilized small-cell BSs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**BS Sleeping Techniques** & **Approach** & **Key Points/Description** \\ \hline Mobile User Association (MUA) & Mobile users transfer themselves from sleeping BS to the nearest BS & Maximum energy efficiency is dependent on the channel state information and access conditions of the nearest BS \\ \hline Self-Organizing Network & BSs shared their traffic conditions with other BSs and then automatically configured themselves for sleep/active modes & While preserving QoS requirements, this technique aims to minimize the active BSs through collaboration among multiple small cells. \\ \hline Cell Breathing/Cell Zooming & BSs continuously monitor the traffic conditions and adaptively change their coverage regions concerning traffic requirements & EE can be optimized by taking computationally complex zooming algorithms for BS cooperation \\ \hline Small Cells Deployment or HetNets & Macrocells should remain in active mode while small BSs can go asleep & Optimal density deployment of both macro and small-cell BS is desired \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of BS sleeping techniques. Figure 5: State-of-the-art switch-off methods for small-cell BS.