# Interference Aware Resource Control for 6G-Enabled Expanded IoT Networks
## 1 Introduction
There are different network operations of cell-free systems, namely distributed and centralized. The signal processing takes place at the CPU in centralized operation where the AP acts as remote relays and send the information to the CPUs for processing [27]. In distributed operation, all the signal processing takes place at the AP. In order to provide stringent quality-of-service (QoS) to all the user nodes, a cell-free approach is used. It allows the coordination of a large number of distributed APs in a given coverage area for serving the enormous wireless devices [28]. Cell-free systems with single antenna nodes and APs are evaluated for performance in [29] for spectral efficiency. It is observed that, by equipping multiple antennas at the APs in a cell-free framework, the spectral efficiency can be further increased [30; 31; 32]. The cell-free approach is used recently in an expanded IoT network to provide extended communication support to the wireless nodes [33]. It aims to propose a scalable IoT network powered by cell-free method such that the energy efficiency is maximized using suitable power control. The role of the cell free system in sustainable IoT networks is highlighted in [34], where the energy is harvested to reduce the total energy consumption. In cell-free IoT networks, the beamforming is used in [35] to maximise the achievable rate using simultaneous wireless information and power transfer (SWIPT). The literature gives the comparison of conventional massive MIMO system with cell-free massive MIMO system [36; 37; 38]. It is highlighted that cell-free approach is more suitable for offering massive connectivity and high energy efficiency. The use of distributed APs in cell-free systems against the collocated antennas in massive MIMO results in improved performance. Different propagation environments are considered to carry out the performance comparison; for example, [39] assesses the cell-free IoT network with the Rayleigh channel model in addition to [40; 41]. The signal processing in cell-free networks and their performance evaluation are carried out in [42; 43; 44; 45]. The transmit precoding is considered in [42; 43], which also uses optimal allocation of training symbols to achieve cooperative clustering. The channel estimation in cell-free networks is highlighted in [44], which aims for better efficiency with reduced normalized mean square error (NMSE). The use of receive combiners in cell-free networks for data detection results in the maximum gain [45]. The role of beamforming for minimum system interference is explored in [46]. The optimization of power control algorithms in the uplink of cell-free IoT systems is considered in [47] with efficient scalability analysis [19]. To serve the massive IoT nodes, the main challenge in a cell-free network is to coordinate or manage the APs and nodes optimally. The cooperation among the APs for coherent transmission with the wireless nodes need to be planned in such a way that interference is reduced. The user access for resources and associated APs requires efficient coordination and optimization such that the network throughput is improved in massive access scenarios. The intelligent algorithms involved in the optimization problems are reviewed in [48] for various application domains. For a multi-UAVs assisted communication scenario, ref. [49] investigates a task reallocation mechanism using optimization algorithm. In this paper, a cell-free IoT network is proposed to meet the massive access needs of the enormous wireless nodes. For efficient network performance, a scheduling algorithm is proposed such that the interference due to neighbouring nodes and APs is minimised. The scalable precoding schemes are used under different cell-free network operations for maximum achieved spectral efficiency. The proposed communication scenario with an interference-aware scheduling algorithm is compared with system network incorporating random scheduling.