Non-orthogonal multiple access (NOMA)-based cognitive heterogeneous multi-homing networks is a very important scenario in the future wireless networks. In this work, we formulate a joint video packet assignment, power control and user scheduling problem as a mixed integer non-linear programming (MINLP) to maximize the total video transmission quality for cognitive multi-homing heterogeneous NOMA networks, which is subject to the maximum accessed number of secondary users at each subchannel, video encoding characteristics, maximum interference power constraint and total available power constraint. For the joint video packet assignment, power control and user scheduling problem, we divide it into a video packet assignment subproblem, a power control subproblem and a secondary user scheduling subproblem for cognitive multi-homing heterogeneous NOMA networks. Firstly, the secondary user scheduling algorithm is proposed using the greedy method. Then, we utilize successive convex approximation (SCA) method to transform the power control subproblem into a convex programming problem, and an approximated optimal power control algorithm is proposed with the dual decomposition method. Finally, a heuristic video packet assignment algorithm is designed, which utilizes the auction theory. Numerical simulation results demonstrate that the proposed algorithms not only improve the video transmission quality, but also enhance the total throughput of cognitive multi-homing heterogeneous NOMA networks.

To exploit the power domain diversity, heterogeneous non-orthogonal multiple access (NOMA) networks improve the spectrum efficiency for Internet of Things (IoT). For video traffic transmission, we formulate a resource allocation and assignment problem as a mixed integer non-linear programming (MINLP) subject to video encoding characteristics, maximum number of accessed devices, max-min fairness criterion, and total available energy of each device. To solve the resource allocation and assignment problem over heterogeneous NOMA networks, two subproblems are formulated, i.e., a packet assignment subproblem for video traffic and a joint device allocation and power control subproblem. Firstly, the joint device allocation and power control subproblem is transformed into a bi-convex programming with successive convex approximation (SCA) method. Then, an optimal device allocation and power control solution is obtained via dual decomposition method. Finally, a heuristic packet assignment algorithm via greedy method is presented for video transmission traffic. In numerical simulation, we can see that the proposed algorithm guarantees the max-min fairness among different devices, and improves the minimum and average video transmission quality over heterogeneous NOMA networks.

In this work, an uplink secondary Internet of Things (IoT) device scheduling and power allocation problem based on imperfect channel state information (CSI) and imperfect spectrum sensing is investigated for industrial cognitive IoT over cognitive heterogeneous non-orthogonal multiple access (NOMA) networks. The joint secondary IoT device scheduling and power allocation problem maximizes the network throughput subject to total power constraint at each secondary IoT device, proportional fairness transmission rate among different secondary IoT devices, maximum number of accessed secondary IoT devices for each subchannel, and interference power threshold constraint at each primary base station (BS). Firstly, successive convex approximation method is adopted to transform the original resource allocation problem into a bi-convex programming problem. Then, we employ the dual decomposition method to analyze the secondary IoT device scheduling criterion and obtain the power allocation close-form expression. Finally, a joint power allocation and secondary IoT device scheduling algorithm with the proportional fairness criterion is proposed. Numerical simulation results demonstrate that the throughput and fairness for the proposed algorithm are better than that of other resource allocation algorithm significantly.