# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
### Related Work  
Since 5G is committed to achieving communication with higher speed, larger capacity, and ultra-low delay, in previous studies, many scholars have devoted themselves to studying how to improve the system rate. To further enhance the spectral efficiency, the author in [14] combined multiple input multiple output (MIMO) technology with a non-orthogonal multiple access system. Furthermore, they transformed the weighted sum rate maximization problem, constructed auxiliary variables, and proposed an iterative algorithm. Compared with the MIMO-NOMA system with an equal power allocation algorithm, the MIMO-NOMA system with the proposed algorithm has a higher weighted sum rate. The authors in [15] studied multi-user systems and rate maximization under perfect CSI. Firstly, the author proposed a sub-optimal low complexity subchannel allocation scheme to allocate each user to the appropriate subchannel. Secondly, the fractional transmit power allocation (FTPA) algorithm was used between subchannels, and the closed form solution of power allocation of multiplexing users was obtained by constructing the Lagrange function in subchannels. Aiming to maximize the weighted sum rate, some scholars studied the subchannel and the power allocation of the multi-carrier non-orthogonal multiple access (MC-NOMA) system in [16; 17]. In recent years, the wireless network energy consumption has been enormous, and effectively using resources to achieve green communication is imminent [18]. The author of [19], taking into account the incomplete CSI, transformed the probability problem of maximizing the energy efficiency of the downlink NOMA heterogeneous network into a non-probability problem. They obtained the approximate solution of the power allocation factor through the dichotomous search algorithm and convex sequence programming. In [20], the authors proposed multi-objective joint optimization problems, such as improving spectral efficiency, meeting transmission power constraints, and improving energy efficiency. In addition, the dual technique was used to solve the problem. However, when evaluating the performance of this scheme, the authors only introduced the implementation effect separately. There were no comparisons and explanations with other methods. Aiming to maximize the energy efficiency of the NOMA system, the authors solved the non-convex optimization problem by subchannel-user matching and power allocation step by step in [21]. Based on the matching theory, this paper proposes a suboptimal sub-channels bidirectional matching algorithm with users. The main idea of the algorithm is that the user, according to the channel gain of each channel on the descending sequence to the highest priority sub-channels, post-matches the request according to whether the current maximum subchannels reject or accept based on the energy efficiency. Although the algorithm can obtain good matching results, the user can only passively select the sub-channel with poor channel quality at the later stage of the user and sub-channel assignment. Due to the relatively high complexity and high computational overhead of previous resource allocation schemes, many researchers have begun to apply heuristic algorithms with increased flexibility and low complexity to the resource allocation problem [22; 23; 24]. Compared with exact algorithms, heuristic algorithms are not guaranteed to find the best solution. However, they can discover near-optimal solutions with appropriate resources. Among them, the particle swarm optimization algorithm (PSO), which was first proposedby Dr. [PERSON] and Dr. [PERSON] in the study of the flight foraging behavior of birds, is a swarm intelligence optimization algorithm [25; 26]. In [27], the authors proposed to apply the particle swarm optimization algorithm to the orthogonal frequency division multiplexing access (OFDMA) adaptive resource allocation problem.