## 2 Related Work
[29] suggested channel-aware transmission scheduling, considering the duration of each slot and channel conditions and adjusting the amount of data to be transmitted. In [30], a deep reinforcement learning (DRL) algorithm is introduced, delayed-reward deep Q-network (DR-DQN), to improve throughput degradation due to long propagation delays in underwater acoustic networks (UANs). Delayed-reward DRL multiple access (DR-DLMA) is proposed, aiming for an optimal channel access strategy that maximizes network throughput by efficiently utilizing available time slots resulting from long propagation delays. In [31], authors suggest prediction of the channel state by capturing temporal and frequency correlations in a one-dimensional convolutional neural network (CNN). In [32] a time slot allocation scheme for clustered underwater acoustic sensor networks, utilizing physical layer information to minimize energy consumption caused by unnecessary retransmissions. This enhancement aims to extend network lifetime and improve throughput. To streamline the process and reduce overhead and computational complexity. In their two-phase approach, first each member node autonomously determines the number of required time slots for the next intra-cluster cycle by solving a Markov decision process (MDP). Second, the cluster head optimizes scheduling decisions based on channel quality and an urgency factor. Despite ongoing research efforts, existing studies mainly focus on the improvement of throughput and energy utilization, considering only the channel state and fairness. On the other hand, the impact of energy harvesting in underwater medium has proven its importance in the design of communication protocols by extending the operational lifetime of underwater sensor devices considerably [14]. A noteworthy study conducted by [PERSON], [PERSON] et al. [16], focused on the improvement of throughput and energy harvesting for solar-powered and clustered WSNs. This work proposes a multi-agent reinforcement learning algorithm to maintain nodes in an energy-neutral state by balancing the consumed and harvested energy of nodes. Yet, this study focuses on the WSNs, their peculiar characteristics underwater, and the amount of available ambient energy that needs to be taken into account for UASNs. A decentralized reinforcement learning approach for random access is proposed to improve the throughput of the network, considering energy harvesting for sustainable data collection from EH-UASNs [15]. In this work, a multi-agent cooperative learning algorithm is used in the adaptive choice of contention window size to optimize throughput while maintaining fairness in the network. However, the authors focused on the throughput and the reward function, considering only the fairness and throughput of the network. Increasing energy harvesting utilization is not investigated in this study. When considering clustered UASNs and TDMA-based protocols, it is imperative to allocate resources effectively to ensure reliable and efficient operation. Therefore, time slot allocation methods for underwater sensor networks need to be robust and accurate, as this greatly affects communication efficiency. Extensive research has been conducted on medium-access strategies aimed at enhancing energy efficiency in UASNs. Notable focus has been placed on reducing network latency, managing traffic, and minimizing energy consumption through various mechanisms such as time synchronization, power control, and sleep modes. However, these studies focus on optimizing energy efficiency rather than maximizing energy harvested from the environment. In the literature, a significant gap persists regarding MAC protocols specifically tailored to optimize and maximize energy harvesting in UASNs, accounting for the distinctive challenges posed by underwater environments.