###### Abstract
Efficient, fair, and coordinated resource sharing is essential for upholding network performance. In underwater acoustic networks (UASNs), the significance of MAC protocols is further emphasized by the distinct challenges posed by the underwater environment. These challenges encompass dynamic propagation delays, restricted bandwidth, modest communication rates, and the substantial energy consumption of underwater devices. Moreover, the ever-changing network topology and charging hurdles add complexity to ensuring stable communication, highlighting the necessity for addressing these aspects in protocol designs such as the MAC layer [7]. MAC protocols for UASNs are divided into contention-free and contention-based categories [8]. Contention-free protocols, such as TDMA, FDMA, and CDMA, assign dedicated resources for communication, with TDMA being renowned for its efficient data throughput via centralized scheduling [9]. However, these protocols rely on global network knowledge, which is difficult to obtain in dynamic underwater environments. Contention-based protocols, including handshaking-free and handshaking-based types, aim to minimize collisions in a distributed manner [10; 11]. While handshaking-based protocols address the hidden-terminal problem, their effectiveness is limited by long underwater propagation delays, reducing acoustic channel efficiency. The ALOHA-Q protocol has been modified for underwater acoustic networks, implementing asynchronous operations and decreasing the number of time slots in each frame to enhance channel utilization [12]. In [13], the authors employed the DR-DLMA protocol, leveraging the DQN algorithm to determine an efficient transmission mode in a heterogeneous network with diverse protocol nodes. Figure 1 depicts structural design of UWSN that includes AUV, sensor nodes for monitoring and courier node for collecting and receiving data from deployed nodes. Courier node along with AUV also act as a bridge between sensor nodes and base station (BS). AUV/Courier node acting as sender incorporate preamble for better synchronization and sensor nodes acting as receiver incorporates Wake up and ACK message for efficient MAC  
protocol design in dynamic underwater environment.To address these obstacles, advanced MAC protocols must be designed to optimize resource allocation, enhance network reliability, and ensure efficiency in highly dynamic and resource-constrained underwater environments. Developing such protocols is essential for the seamless operation of UWSNs, enabling efficient resource management, reliable data transmission. This paper introduces a novel DQN-optimization based DAWPC-MAC receiver-initiated MAC protocol that minimizes energy consumption by allowing the courier node as transmitter (Tx) and receiver node (Rx) to control its wake-up schedule, ensuring synchronization with other transmitter (Tx) and receiver (Rx) nodes, and prioritizing critical data traffic. The integration of DQN-based optimization, the protocol can adapt to varying network conditions, optimizing power consumption while maintaining communication reliability. Our main focus is on DQN, a robust method in reinforcement learning. The goal is for the agent to interact with its environment and improve cumulative rewards over time. Unlike traditional Q-learning, DQN utilizes a Deep Neural Network (DNN) to approximate the Q-value function, allowing the agent to traverse complex, multi-dimensional state spaces. This strategy-driven approach enables the system to continuously refine its decision-making by learning optimal actions through experience and adapting its behavior based on past environmental interactions. The use of DQN in MAC protocols for courier nodes as receiver nodes offers significant benefits by enabling adaptive decision-making. DQN helps courier node as receiver nodes optimize resource allocation, minimize transmission conflicts, and dynamically adjust to fluctuating network conditions.