Device to device (D2D) communication technology is the main component of future communication, which greatly improves the utilization of spectrum resources. However, in the D2D subscriber multiplex communication network, the interference between communication links is serious and the system performance is degraded. Traditional resource allocation schemes need a lot of channel information when dealing with interference problems in the system, and have the problems of weak dynamic resource allocation capability and low system throughput. Aiming at this challenge, this paper proposes a multi-agent D2D communication resource allocation algorithm based on Advantage Actor Critic (A2C). First, a multi-D2D cellular communication system model based on A2C Critic is established, then the parameters of the actor network and the critic network in the system are updated, and finally the resource allocation scheme of D2D users is dynamically and adaptively output. The simulation results show that compared with DQN (deep Q-network) and MAAC (multi-agent actor–critic), the average throughput of the system is improved by 26% and 12.5%, respectively.

The sudden explosive growth of data in intelligent devices and existing communication networks has brought great challenges to existing communication networks. On the one hand, D2D (device to device) technology greatly improves the utilization of spectrum resources; on the other hand, it improves the communication quality of users. It has become an important part of the future communication network. Aiming at the problem that the existing D2D communication network system has complex user interference, and the communication quality of cellular users is difficult to guarantee, a D2D communication network interference coordination scheme based on improved Stackelberg is proposed. Using resource allocation and power control to solve the interference coordination problem, this paper proposes an improved Stackelberg model based on DQN (deep Q network), establishes the master–slave game between cellular users and multiplexing resource users (D2D users; relay communication users), optimizes the cost parameters in the Stackelberg mode and improves the transmission power and resource allocation scheme of multiplexing resource users. The simulation results show that compared with similar algorithms, the algorithm proposed in this paper has the best performance in guaranteeing the QoS of cellular users in the system and has good interference management capability for D2D communication networks.

Device-to-device(D2D) communication combined with non-orthogonal multiple access(NOMA) can further improve the quality of service for future communications systems and increase spectrum utilization and connection density in cellular networks. In this paper, an effective power allocation scheme is designed to optimize the performance of a D2D communication system based on NOMA for imperfect Successive Interference Cancellation(SIC) decoding. To solve the non-convexity of the problem due to integer constraints and coupling variables, an alternate optimization algorithm was designed to obtain the optimal solution of each subproblem by Lagrange duality analysis and the sub-gradient descent method. Finally, the numerical simulation results demonstrate the performance advantages of the proposed joint optimization algorithm for channel resource allocation and power control in energy efficiency.

With the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the spectrum and with high data demand in future wireless networks. However, the efficient coordination of these techniques in complex and changing 3D environments still faces many challenges. To this end, this paper proposes a NOMA-based multi-UAV-assisted D2D communication model in which multiple UAVs are deployed in 3D space to act as airborne base stations to serve ground-based cellular users with D2D clusters. In order to maximize the system throughput, this study constructs an optimization problem of joint channel assignment, trajectory design, and power control, and on the basis of these points, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network (DH-MDQN) algorithm. The dynamic hypergraph method is first used to construct dynamic simple edges and hyperedges and to transform them into directed graphs for efficient dynamic coloring to optimize the channel allocation process; subsequently, in terms of trajectory design and power control, the problem is modeled as a multi-agent Markov Decision Process (MDP), and the Multi-Agent Deep Q Network (MDQN) algorithm is used to collaboratively determine the trajectory design and power control of the UAVs. Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement.