Joint energy efficient subchannel and power optimization for a downlink NOMA heterogeneous network

F Fang, J Cheng, Z Ding - IEEE Transactions on Vehicular …, 2018 - ieeexplore.ieee.org
IEEE Transactions on Vehicular Technology, 2018ieeexplore.ieee.org
Non-orthogonal multiple access (NOMA) has been considered as a key technology in the
fifth-generation mobile communication networks due to its superior spectrum efficiency.
Since the heterogeneous network has been emerged to satisfy users' explosive data rate
requirements and large connectivity of mobile Internet, implementing NOMA policy in
heterogeneous networks (HetNets) has become an inevitable trend to enhance the 5G
system throughput and spectrum efficiency. In this paper, we aim to maximize the entire …
Non-orthogonal multiple access (NOMA) has been considered as a key technology in the fifth-generation mobile communication networks due to its superior spectrum efficiency. Since the heterogeneous network has been emerged to satisfy users’ explosive data rate requirements and large connectivity of mobile Internet, implementing NOMA policy in heterogeneous networks (HetNets) has become an inevitable trend to enhance the 5G system throughput and spectrum efficiency. In this paper, we aim to maximize the entire system energy efficiency, including the macrocell and small cells, in a NOMA HetNet via subchannel allocation and power allocation. By considering the co-channel interference and cross-tier interference, the energy efficient resource allocation problem is formulated as a mixed integer nonconvex optimization problem. It is challenging to obtain the optimal solution; therefore, a suboptimal algorithm is proposed to alternatively optimize the macrocell and the small cells resource allocation. Specifically, convex relaxation and dual-decomposition techniques are exploited to optimize the subchannel allocation and power allocation. Moreover, optimal closed-form power allocation expressions are derived for small cell and macrocell user equipments by the Lagrangian approach. Simulations results show that the proposed algorithms can converge within ten iterations and can also attain higher system energy efficiency than the reference schemes.
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