# Power Optimization Model for Energy Sustainability in 6G Wireless Networks  
[PERSON]  
1 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India; [EMAIL_ADDRESS] (A.T.); [EMAIL_ADDRESS] (N.S. )1 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India; [EMAIL_ADDRESS] (A.T.); [EMAIL_ADDRESS] (N.S. )1 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India; [EMAIL_ADDRESS] (A.T.); [EMAIL_ADDRESS] (N.S. )1 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India; ashu.taneja@chitkaran. Instead, cell-free networks are emerging as the promising technology for 6G-enabled communication scenarios. Cell-free massive MIMO is a new approach in which a number of access points (APs) are deployed in a given coverage area to provide service to a number of users distributed in that area [14]. As the name suggests, there are no cell boundaries in this network and each AP serves a subset of users; in other words, each user is served by a subset of APs. The APs cooperate amongst themselves and serve the users through joint coherent transmission and reception, resulting in increased signal-to-noise ratios (SNRs). There are two approaches to cell-free networks for AP subset selection. In the user-centric approach, it is the user who makes the decision of selecting an AP based on some performance parameter [15; 16], while in network-centric approaches, it is the network that selects the AP subsets by dividing them into small clusters [17]. Each AP in a network is connected to the central processing unit (CPU), also called cloud-edge processors [18]. The data encoding and decoding takes place at the centralized CPU, which gathers information from the APs. The network operation where all the signal processing takes place locally at the AP is called distributed operation. If the signal processing, including precoding and receive combining, occur at the CPU, this is centralized operation. The cell-free network's performance evaluation is carried out in the literature with signal processing techniques including receive combining in the uplink [19], precoding in the downlink [20], beamforming [21], channel estimation [22], and pilot allocation [23]. The cell-edge performance of conventional multi-antenna systems are improved with the cell-free approach [24]. Cell-free massive MIMO systems with single-antenna users and APs are considered in [25]. It is estimated that the spectral efficiency improves by five times using the cell-free approach. Cell-free systems with multi-antenna users and APs lead to enhanced spectral efficiency, as highlighted in [26; 27; 28]. In contrast to conventional systems, where all APs serve the users, the cell-free approach leads to a reduced energy overhead by allowing a subset of APs to serve the users. Moreover, it offers efficient power optimization through the intelligent management of power resources [29]; through a number of power-control strategies [30], it enables the users in the uplink to cut down its power to specific APs. [PERSON] et al. [31] considers power allocation in the downlink, where different APs allocate different transmit powers to different users. The power allocations according to the users' channels and traffic loads are considered in [22; 32], respectively, resulting in reduced power consumption and energy-efficient scalable networks. The joint optimization of the power control and scheduling of APs is carried out in [33], while the energy efficiency optimization is carried out in [34; 35]. The energy-saving solutions applicable for cell-free networks to achieve the huge energy demands also include utilizing the concept of energy harvesting [36], wireless power transfer [37; 38], and simultaneous wireless information and power transfer (SWIPT) [39].