Fairness-Aware Throughput Maximization for Underlaying Cognitive NOMA Networks

Abstract

To improve the radio spectral efficiency for 5G and beyond, novel radio access techniques need to be designed to accommodate unprecedented number of connected devices, and non-orthogonal multiple access (NOMA) has become a promising candidate. Additionally, power allocation and NOMA-secondary user(SU) assignment technology is an efficient way to enhance the resource utilization efficiency at the power domain and the spectral domain for underlaying cognitive NOMA networks. In this paper, firstly, a joint power allocation and SU assignment problem is formulated for NOMA downlink transmission in an underlaying cognitive radio network. The worst-case achievable rate for the NOMA-SU is maximized. To solve this mixedinteger non-linear programming (MINLP) problem, we divide the original optimization problem into two sub-problems: NOMA-SU assignment and power allocation. Next, a heuristic algorithm is adopted to solve the NOMA-SU assignment subproblem, and successive convex approximation (SCA) based method is utilized to design a suboptimal power allocation algorithm. Furthermore, an alternative joint NOMA-SU assignment and power allocation scheme is proposed with its average computational complexity analysis given. Finally, numerical results show that the total throughput for the proposed algorithm outperforms more than 30 percent compared with an existing benchmark scheme at least.

UAV-Assisted Cooperative & Cognitive NOMA: Deployment, Clustering, and Resource Allocation

“…We should note that the above works do not apply to CR-NOMA schemes as they do not account for interference temperature constraint (ITC) of the primary network. Further, Xu et al studied a joint power allocation, and SU assignment problem for underlay CR-NOMA networks under channel uncertainties in [21]. Although generated results showed that the proposed approach gains higher throughput than that of the Mitran algorithm, their work did not provide a clustering solution for either PUs or SUs.…”

Energy Efficiency and User Capacity Optimization of Cognitive MIMO Systems Via the SCMA-Based Nonorthogonal Time Slot Allocation

“…Recently, SCMA technology has been discovered to be a NOMA technology with great potential because of its excellent advantages of improving spectrum efficiency. In the literature, most studies have focused on combining NOMA technology with CR technology to improve EE and user access [19][20][21][22][23][24]. In Reference [19], a CR-NOMA network with wireless information and power transmission (SWIPT) is considered under the nonlinear energy collection model to improve EE and spectrum efficiency (SE).…”

“…Reference [22] has designed a new secure CR-NOMA transmission strategy that pairs the PUs and SUs according to the channel gain to improve the performance of the secondary system. In Reference [23], a fair-aware joint power allocation and SU allocation problem based on downlink cognitive NOMA networks is studied, in which an alternative optimization scheme is adopted to enhance the system capacity. Reference [24] studied the robust resource allocation and sub-channel allocation to maximize the total EE of SUs from the perspective of EE-robustness trade-offs in the multi-user cognitive NOMA network.…”

“…Although NOMA-SU allocation technology is an effective method, it can enhance the resource utilization efficiency in the power domain and spectrum domain to support the cognitive NOMA network [23]. However, it is difficult to guarantee that EE can be improved while accessing more users and reducing interference.…”

“…In recent years, the combination of CR and NOMA can further effectively improve energy efficiency, and spectrum utilization has been widely concerned [19][20][21][22][23][24]. But, its user capacity is limited, and there is even some interference from secondary systems.…”