## 7 Discussion  
The deployment of SIC in real-world environments presents a series of significant challenges, primarily related to hardware requirements. High-precision components, such as analog-to-digital converters (ADCs), digital-to-analog converters (DACs), and mixers, are crucial for accurate interference cancellation. However, these components come with high costs and substantial energy demands, particularly for wideband signals. Additionally, nonlinearities in Radio Frequency (RF) chains, such as amplifier imperfections and phase noise, complicate the cancellation process, making it difficult to maintain optimal performance. Achieving real-time, low-latency SIC requires considerable computational power, which in turn increases power consumption. These hardware-related challenges make SIC implementation complex and energy-intensive, especially for mobile devices where performance and battery life must be carefully balanced.  
In dense node networks, interference management becomes progressively more difficult as the number of devices increases, threatening communication quality. The high cost of deploying infrastructure, particularly for backhaul in both urban and remote settings, adds another layer of complexity. Scaling the network while ensuring reliable communication demands efficient load balancing and synchronization across nodes. Energy consumption is another critical concern, particularly in dense networks where nodes must operate efficiently to minimize power usage. To address these challenges, emerging solutions such as AI-driven interference management, innovations in energy-efficient hardware, and reconfigurable intelligent surfaces (RISs) show promise in enhancing the practicality and efficiency of SIC and dense network deployments in real-world scenarios.  
In the simulations, several trends related to utility values and system scaling can be observed. One interesting trend is that players who achieve higher utilities typically have better channel conditions, as indicated by the channel gain matrix. Players with stronger self-channel gains experience higher SINR, leading to higher utility. In contrast, players with weaker channel conditions or higher interference from other players tend to have lower utility values. As the transmission powers evolve over time, players adjust their transmission power to maximize their utility, which results in fluctuations depending on the interference levels and channel conditions. In particular, the presence of SIC leads to more effective suppression of interference, allowing players to achieve higher utilities compared to the non-SIC case.  
As the system scales with an increased number of players, the interference between players becomes more significant, and the system becomes more difficult to manage. This is reflected in the increasing complexity of the optimization process, as each player's strategy must take into account the interference from all other players. The potential function, which sums the utilities of all players, also exhibits some fluctuations as the system moves toward equilibrium. Larger systems with more players may take longer to converge and require more computational resources. The convergence rate and the final transmission powers depend on the number of players, and as more players are added, it may become harder to achieve a global optimum due to the increasing interference and competition for resources.