# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 2 Related Works  
In the literature, the weighted sum rate and sum rate are the metrics most utilized to measure the performance of NOMA. The weighted sum rate performance has been adopted by several research studies [17; 18; 19; 20]. The author in [17] proposed an optimal power allocation method to maximize the weighted sum rate with quality of service (QoS) constraints for the downlink NOMA system. The proposed method achieved lower computational complexity without consideration for the power transmission constraint in the optimization process. A greedy search-based user pairing with optimal power allocation method for maximizing the weighted sum rate was also presented in [18]. Although high achievable weighted sum rate performance was obtained, the QoS constraints were not considered in the optimization process formulation. In [19], single-carrier power control (SCPC) algorithm and single-carrier user selection (SCUS) algorithm were adopted for user power and user selection optimization process. The NOMA-FTPA technique that assigns the user's power based on the channel gain condition was used for comparison. The results showed that the proposed algorithms achieved higher weighted rate with higher computational complexity when compared with the NOMA-FTPA technique. The authors in [20] proposed an optimal joint subcarrier and power allocation technique in a multicarrier multiple-input single-output NOMA (MISO-NOMA) network to maximize weighted system throughput with the consideration of QoS requirements. The results showed that multicarrier MISO-NOMA had a higher throughput than MISO-OMA. The sum rate metric has also been utilized by several studies [21; 22; 23; 24; 25; 26]. Fixed power allocation (FPA) method was utilized for power optimization in order to improve the user's data rate [21]. The proposed method does not guarantee an optimal performance, and thus it is not appropriate for practical situations. The power allocation problem was analyzed in NOMA with the visible light communication (NOMA-VLC) system [22]. The authors proposed using the equal power allocation (EPA) technique to serve the users. In term of sum rate, the proposed technique outperforms the FPA and gain ratio power allocation (GRPA); however, it is not an optimal solution. An optimal power allocation technique with fairness constraints based on the Okumura Hata model was introduced in [23] to maximize the sum rate performance. The proposed technique provides higher throughput performance with higher computational complexity based on all available powers that satisfy the stipulated fairness constraints. In [24], the power optimization technique with the QoS requirement called improper Gaussian signaling (IGS) was presented, with the aim of maximizing the sum rate of the NOMA system with imperfect SIC. The results showed that IGS achieved a high sum rate; however, the power constraints were not considered in the optimization. The work in [25] proposed the Lagrangian duality function to maximize the average sum rate for full CSI. Besides, the power allocation problem was addressed analytically for partial CSI. The proposed function achieved higher average sum rate performance; however, the authors did not consider any user pairing method to achieve optimal performance. In [26], a price-based power allocation and user-pairing-based Stackelberg game theory methods were proposed to maximize the sum rate lacking the QoS constraint for the optimization process. As the number of wireless users and the volume of data traffic increase, the energy consumption of wireless systems will consequentially increase. Thus, attention must be paid to energy-efficient design in next-generation wireless communication systems. In this context, the resource allocation problem has been considered in the literature for the aim of achieving high energy efficiency performance in NOMAsystem design, as seen in research publications [27; 28]. The authors in [27] formulated a resource allocation problem to maximize energy efficiency using supermodular game theory.