# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
## 1 Introduction
### Motivation and Contributions  
In this paper, we consider sub-channel assignment and power management problem in multi-user NOMA system to maximize the sum capacity. The main motivation of this work is to enhance the previous work as presented in Reference [12]. Note that the work provided in Reference [12] only consider power allocation for two user network to enhance the sum capacity. Their approach was to satisfy the minimum capacity of first user and then allocate the remaining power to the second user to maximize its capacity. Different from Reference [12], our technique first allocate power to both users to satisfy their minimum capacity requirements and then optimize the remaining power for them to maximize the sum capacity. In addition, we apply two novel algorithms of sub-channel allocation to our proposed power management scheme and to the one in Reference [12]. These algorithms are based on user channel condition and user minimum capacity requirement. Thus, we use Reference [12] as a benchmark scheme in our work. 1. Consider a NOMA network using power multiplexing, where a BS intends to communicates with \(K\) users through \(M\) sub-channels. We formulate a resource management problem for sub-channel assignment and power allocation. Our objective is to maximize the sum capacity of the system subject to the user minimum capacity requirement. 2. The problem of resource management decouples into two sub-problems where we fist provide two sub-optimal algorithms for sub-channel assignment under the fixed power of BS. We design these algorithms based on user channel condition and user minimum capacity requirement, respectively. For efficient power allocation, we then transform the optimization problem into standard convex optimization and for any given sub-channel allocation, we exploit dual theory to obtain the optimal solution. 3. For a fair comparison, we also consider the work in Reference [12] as a benchmark for power allocation. Reference [12] first satisfy the minimum QoS of one user and then allocates the remaining power to another user to maximize its capacity. Different from the work in Reference [12], we first satisfy the minimum capacity requirements of both users and then optimize the remaining power among these users. 4. For simulation results, we apply the sub-channel assignment algorithms to the proposed power allocation scheme and the one in Reference [12]. We also provide the results of the proposed power management technique and the power management technique of Reference [12] for random sub-channel assignment. Results demonstrate that our NOMA technique provides a higher capacity than that in Reference [12]. The reminder of this paper is organized as follows: We discuss the downlink NOMA model and various formulation steps for resource management in Section 2. Section 3 provides resource management with fixed power control while Section 4 presents resource management with optimal power control, respectively. We evaluate and discuss our results in Section 5. Finally, we conclude our paper in Section 4. Table 1 provides the definition of different abbreviations and notations used in this paper.