## 2 Related Work  
Recently, numerous techniques have been presented in the literature to control downlink transmission power of the femtocell. In [20], a distributed joint resource allocation algorithm has been proposed to mitigate ICI in the two-tier femto-macro network. This algorithm consists of spectrum sensing, transmission mode selection and channel state information (CSI) estimation. The cognitive femtocell senses the unused slots and assigns a set of slots for each user. The slot allocation works is well in conjunction with power allocation centred on geometric programming to enhance the overall performance of the system. In [21], the proposed algorithm gradually reduces the downlink transmission power, whenever they informed about a cell edge macrocell users getting interference from its transmission power. This interference avoidance technique works when it receives information about MUE in the vicinity of the femtocell. Interference avoidance technique enables to deploy multiple femtocells in an indoor environment with higher capacity. The author presented cross-tier signal-to-leakage-plus-noise (SLNR) based Water Filling (CSWF) power allocation algorithm [22] for the mitigation of interference from the femtocell to the macrocell users. Cross-tier SLNR method reduces the major part of cross-tier downlink interference by using a modified Water Filling (WF) power allocation algorithm to determine the transmission power for each Resource Block (RB) of the femtocell. Cognitive power control technique in [23] used to address the interference problem from a femto base station to the nearby macrocell users. The FBS has the ability to sense its surrounding in terms of spectrum sensing and obtain the required information about downlink radio resources of a macrocell user. The FBS then tunes its transmission power efficiently and enhances the system capacity by mitigating interference. In [2], the Stochastic Approximation (SA) algorithm for downlink power control based on the information received through macrocell signalling. The femtocell then updates its downlink transmission power based on this information. The adjustment of Downlink (DL) power is based on the Channel Quality Indicator (CQI) and the ACK/NAK signal. A dynamic power tuning technique presented in [24] is used to tackle the downlink interference from HeNB to MUE. The HeNB tunes its downlink power based on information received from the interfered MUE. This interference mitigation technique helps to reduce the consumption of power and enhance the throughput of MUE. The MUE exists in the close proximity of HeNB, and receives interference from HeNB, to reduce such interference; a power control scheme proposed in [25] based on network listening. In [26], a novel scheme is proposed for interference mitigation in downlink cognitive femtocell networks, which is called joint channel allocation and power assignment. This scheme aims to reduce interference from the femtocell to MUEs and co-tier interference by collaboratively allocating power resources and channels between several femtocells; it depends on the Physical Cluster (PC) and the Virtual Cluster (VC). The writer also presented subcarrier power allocation and a VC-based power budget adjustment algorithm and Hungarian algorithm to better allocation of resources, minimize both co-tier and cross-tier interference in the femtocell. In [27], distributed coordination techniques in the DL of macrocells for controlling ICI caused by a femtocell in two-tier networks, where opportunistically reuse resources is an inherent requirement. The technique emphasis is on the autonomous operation of femtocell by self-organizing deployment. In [28], the downlink power control scheme presented to reduce the interference caused by a femtocell to its neighboring cell users.