# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 2 Related Work  
The objective of this paper is to maximize the aggregated data rate of IoT devices while keeping an acceptable average energy outage probability. There are many works with similar objectives (i.e., to increase the data rate and/or the energy efficiency) in the literature [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23].  
To maximize the data rate and energy efficiency, [PERSON] et al. [8] devised an optimal power allocation algorithm using deep learning in multiple-input multiple-output (MIMO)-NOMA environments. [PERSON] et al. [9] formulated an optimization problem to improve the aggregated data rate and reduce the energy consumption under some constraints such as QoS requirement and maximum transmission power. [PERSON] et al. [10] developed an adaptive power allocation scheme that adaptively selects the combination of a suitable power allocation factor and modulation and coding scheme under the constraint of bit error rate. [PERSON] and [PERSON] [11] formulated an optimization problem on the power allocation for the maximum aggregated data rate and designed a low complexity algorithm for achieving a near-optimal solution to the problem. [PERSON] et al. [12] proposed a two-step scheduling and power allocation scheme to optimize a tradeoff between consumed energy and achievable data rate. [PERSON] et al. [13] formulated an optimization problem to optimize the overall energy efficiency under heterogeneous networks. In addition, they proposed a suboptimal algorithm based on mathematical techniques (i.e., relaxation and dual-decomposition). [PERSON] et al. [14] defined a resource optimization problem for NOMA with the consideration of heterogeneous networks, and designed a heuristic algorithm having low complexity by converting the problem into an equivalent subtractive one.  
[PERSON] et al. [15] formulated a joint optimization problem on the power and time allocation for the maximized energy efficiency of device-to-device communications using NOMA. With similar decision variables, [PERSON] and [PERSON] [16] formulated an optimization problem and divided the problem into two subproblems to develop a heuristics algorithm. [PERSON] et al. [17] investigated a channel and power allocation to optimize the energy efficiency of IoT devices by considering the characteristics of short-packet communication. [PERSON] et al. [18] formulated a resource allocation problem in NOMA-based backscatter networks to maximize the energy efficiency of IoT devices while satisfying the QoS requirement. [PERSON] et al. [19] conducted an analytical study on the performance of NOMA, especially on the achievable data rate. [PERSON] et al. [20] formulated a weighted aggregated data rate maximization problem and converted the formulated problem into a convex problem using mathematical techniques. [PERSON] et al. [21] proposed an adaptive rate NOMA where mobile users adaptively change their modulation and coding scheme (MCS) according to the channel conditions whereas IoT devices exploit the fixed MCS to improve the rate of successful transmissions of the IoT devices. [PERSON] et al. [22] designed a resource allocation algorithm based on the K-means clustering method and matching theory to maximize the aggregated data rate. [PERSON] et al. [23] formulated a joint optimization problem on the trajectory of an unmanned aerial vehicle and the resource allocation to improve the average aggregated data rate. In addition, they developed a suboptimal iterative algorithm to solve the formulated problem.  
However, there is no previous work to optimize the policy on the transmission of IoT devices under the energy harvesting and NOMA environment in a distributed manner.