## 2 Literature Review  
It has been now globally acknowledged that energy-related issues will have a greater impact in the coming years on wireless communication networks. Energy consumption by wireless networks can be minimized in various ways. Broadly, these approaches can be classified into five different categories, listed in Figure 3. Each technique has its benefits and limitations. Upgrading hardware equipment, e.g., power amplifiers, BS site relocation, and reselection, can provide maximum energy saving but requires costly solutions for hardware replacements [18]. Radio transmission optimization techniques, e.g., cooperative relays, cognitive radios, signal resource allocation, and channel coding schemes are comparatively cheaper solutions, but these techniques have high chances of error due to uncertainty and have trade-offs between energy saving and system performance. Re-planning and deployment of new infrastructure like integrated macro and small cells will introduce new challenges to the radio interface. Adoption of renewable resources like solar, hydro, and wind power can serve as a long-term solution but it will come with a high replacement cost, and for current BS, there are limited gains [19]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Technology**} & \multicolumn{6}{c}{**Enabling Solutions**} \\ \cline{2-7}  & **High Data Rate** & **High Capacity** & **Low Energy Consumption** & **High Coverage** & **High** **Coarse** & **High** **Coarse** & **Ref.** \\ \hline HetNets & ✓ & ✓ & ✓ & ✓ & ✗ & ✗ & \\ \hline m-MIMO & ✓ & ✓ & ✓ & ✗ & ✓ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key enabling technologies for 5G. Figure 3: EE techniques in cellular network. Last but not least is the sleep mode techniques, e.g., turning on/off underutilized BS can provide much easier implementations and low-cost solutions for energy efficiency improvements. However, its network modeling will be a difficult task. It can be noticed that out of many EE techniques, sleep mode schemes seem to be the most viable option for cellular networks, as sleep mode schemes do not require any hardware replacement. This makes it a cost-effective and potentially viable solution for making current HetNets more EE [20; 21]. In addition, it is also imperative to notice that before enabling the sleep mode in particular BSs, the intended sleep-enabled BSs must coordinate with other BSs and release its channel resources to active neighboring BSs. The active BS must provide extended data rate services and coverage to users located inside sleeping BSs [22]. The exchange of traffic information between BSs is usually managed by the BS Controller. However, switching a BS to sleep mode will eventually result in an increase in outage probability and call-blocking events, which will result in a deteriorating QoS experienced by users [23]. To improve the energy efficiency of cellular networks, researchers have identified the following categorical sleep-enabling techniques: Figure 3 shows the state-of-the-art small-cell sleep-enabling methods. During sleep mode, appropriate UA can be used to increase network performance. For example, when a BS enters sleep mode, users impacted by this should link to other BSs that have available resources. It means that the affected users should not automatically connect to the BS that provides high SINR if they can obtain BS connectivity with more bandwidth (capacity), low SINR, or a larger distance [2; 24]. Hence, an optimal UA method that maximizes BS cooperation is desired for maintaining acceptable QoS with maximum energy savings. In traditional UA schemes, users tend to associate with the BS that provides the best SINR levels. With this understanding, all the BS will be fully loaded, and this strategy is used to maximize the network throughput. However, HetNets, where small-cell BS is underlaid by macro BS, have much lower transmit power as compared to macro BSs.