User Clustering and Power Allocation for Energy Efficiency Maximization in Downlink Non-Orthogonal Multiple Access Systems

Abstract

Non-orthogonal multiple access (NOMA) has been considered a promising technique for the fifth generation (5G) mobile communication networks because of its high spectrum efficiency. In NOMA, by using successive interference cancellation (SIC) techniques at the receivers, multiple users with different channel gain can be multiplexed together in the same subchannel for concurrent transmission in the same spectrum. The simultaneously multiple transmission achieves high system throughput in NOMA. However, it also leads to more energy consumption, limiting its application in many energy-constrained scenarios. As a result, the enhancement of energy efficiency becomes a critical issue in NOMA systems. This paper focuses on efficient user clustering strategy and power allocation design of downlink NOMA systems. The energy efficiency maximization of downlink NOMA systems is formulated as an NP-hard optimization problem under maximum transmission power, minimum data transmission rate requirement, and SIC requirement. For the approximate solution with much lower complexity, we first exploit a quick suboptimal clustering method to assign each user to a subchannel. Given the user clustering result, the optimal power allocation problem is solved in two steps. By employing the Lagrangian multiplier method with Karush–Kuhn–Tucker optimality conditions, the optimal power allocation is calculated for each subchannel. In addition, then, an inter-cluster dynamic programming model is further developed to achieve the overall maximum energy efficiency. The theoretical analysis and simulations show that the proposed schemes achieve a significant energy efficiency gain compared with existing methods.

Differential Evolution Optimized Non-Orthogonal Multiple Access for Sum Rate Maximization

“…The findings demonstrate that, although more complex than fixed transmit PA, full search PA has the highest performance when compared to the other PA techniques. The authors of [8] focus on an efficient user grouping method and the PA design of downlink NOMA. This work considers an NP-hard problem under extreme and least transmission data and SIC utilization.…”

“…The optimized values of a i are essential to maximize the sum rate given in (8). The optimization problem is formulated as…”

Adaptive Clustering of Users in Power Domain NOMA

“…Although providing a significant increase in spectral efficiency, NOMA systems also increase energy consumption. Therefore, in [21], a solution for user clustering and power allocation was studied to maximize the energy efficiency under maximal transmission power, minimum data transmission rate requirement and SIC requirement. The clustering method has two steps: initially, pairs of users with the most significant differences between channel gains are formed, and each group is associated with a cluster.…”

Non-Orthogonal Multiple Access (NOMA) Dynamic Power Allocation in 5G Networks

“…Furthermore, dissimilar to the lagrangian approach-based power control strategy proposed in Ref. [12], a straightforward and extremely quick iterative power allotment component was presented. The research's contribution can be summarized as follows:…”