## 2 Literature Review
This results in a significant difference in the coverage areas of macro and small-cell BS, ultimately making user association more difficult in EE HetNets. For example, if UA is based on the received signals from BSs, macro BS is more prone to be fully occupied due to its high coverage, antenna gain and transmit power. On the other hand, small-cell BSs will have few users due to their smaller transmit power, antenna gain, and coverage. This is an extremely undesirable situation because the user connected with macro parent BSs will have reduced capacity (bandwidth) compared to the users in the tier of small cells. This situation leads to an inequitable distribution of resources among users and may decrease the network's total sum rate and spectral efficiency [25]. Hence in energy-efficient HetNet UA is one of the major features that may affect many other key performance indicators like QoS, load balancing, interference, etc. [26]. To cater to this issue in HetNets, artificial cell biasing methods are used to maximize load balancing and manipulate UA. In this technique, users are artificially biased to favor small-cell BS over macro cell. However, it is worth mentioning that when a user is artificially inclined to link to a small cell, it will receive strong interference from macro BS. This situation will lead to increased aggregate interference, which certainly affects the QoS parameter experienced by the biased user. Therefore UA biasing has a tradeoff performance with average rate and coverage probability in HetNets [27]. Extensive efforts have been made in the last few years for UA optimization in HetNets [2; 26]. In this research work minimum, biased transmission distance (m-BTD) scheme presented in [26] is adopted. In this method, a user already knows the location of nearby BSs from all tiers along with its location. The user is responsible for computing the relative distance to the nearest BS from each tier. A biasing system is defined which has real numbers [8]. Self-organizing networks (SON) were proposed in 3 GPP standards. Later on, it was implemented in 4G standards including WiMAX and LTE. Robust network management is provided by SON. Cell breathing or cell zooming has the same features as SON but offers greater adaptability. It is a network layer technique in which the size of a small cell is dynamically modified based on data traffic conditions. by changing BS antenna transmission power, tilt angle, or height. From an implementation point of view, it is simple as compared to turning off/on BS. However, small-cell deployment is considered the most recent and robust method to improve EE in wireless cellular networks [22; 25; 28; 29; 30]. This research work is also based on adapting EE in HetNets through a small-cell sleep deployment technique. Figure 4 represents the existing sleep-enabling techniques. The above-mentioned techniques are applied to optimize EE in a single-tier network, i.e., macro only. However, adding a small BS will change the network infrastructure from a homogenous to a heterogeneous network. The main focus of this research is on enhancing energy efficiency in a HetNet environment, comprising small-cell BSs alongside macro BSs. Table 2 represents the summary of BS sleep mode techniques in a cellular network. The majority of research efforts have concentrated on enhancing data throughput, coverage, resource efficiency, and other aspects not directly related to energy efficiency. The performance analysis of HetNet has not been investigated to its full potential from an energy point of view. Within HetNets, when small cells are expected to enter the sleep phase, they typically adopt two main types of sleep methods which are fixed, static, and dynamic sleep methods. Fixed and static sleep methods use a predetermined number of state switches over a set period. In contrast, dynamic sleep mode schemes permit BSs to adjust their states flexibly, responding to parameters such as traffic and channel conditions [7; 20; 31].