Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

Abstract

The increasing in energy consumptions of the current wireless networks, leads towards designing energy-efficient 5G networks. The application of non-orthogonal multiple access (NOMA) in the heterogeneous networks (HetNets) improves the spectrum utilization with the cost of efficient resource allocation. Hence, this paper proposes optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNets. In the proposed optimization process, the considered constraints are the user's transmission rate, transmit power budget at the base station (BS), and the interference. The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The Greedy Algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. Simultaneously, the max-min energy efficiency optimization approach is employed to maximize the minimum energy efficiency of the femtocell users to achieve the optimal power allocation solution. The mathematical formulation of the max-min energy efficiency is a nonconvex fractional programming problem and is intractable. Thus, the fractional programming theory is adopted to transform the problem into a sequence of subtractive form, followed by the Sequential Convex Programming (SCP) approach to determine the optimal solution. Simulation results show that the proposed NOMA with optimal power allocation method using SCP and GA (NOMA-SCP-GA) achieves fair energy efficiency performance with lower complexity compared to the benchmark methods. Moreover, the minimum energy efficiency of the femtocell user is 38.22% higher than NOMA with Difference of Convex programming (NOMA-DC). The NOMA-SCP-GA method can assure 5G capability demands.

Spectral and energy efficiencies maximization in downlink NOMA systems

“…the constraint of ( 𝑆𝐸 𝑚𝑖𝑛 ) can be replaced by the constraint of minimum transmit power (𝑝 𝑚𝑖𝑛 ). As a result, it satisfies that  𝑆𝐸 𝐷𝐿 (𝑝 𝑚𝑖𝑛 ) =  𝑆𝐸 𝑚𝑖𝑛 , and the corresponding minimum sum rate is indicated as 𝑅 𝑚𝑖𝑛 = W  𝑆𝐸 𝑚𝑖𝑛 according to (8). In addition, the minimum data rate of each user is also considered.…”

“…Figure 1. NOMA vs TDMA (OMA) [7] In NOMA-based systems, the spectral efficiency (SE) (achievable sum-rate) is logarithmically increased with maximum transmit power, while EE is reversely proportional to the total consumed power which is an affine function of the maximum transmit power [8]. As a result, there is a non-negligible trade-off relationship between SE and EE.…”

Tandem RL Framework for Sum Rate Enhancement in NOMA-UAV Network

“…Eventually, the problem (23a) is now reduced to (24a) and has become a multiple choice knapsack problem (MKCP) [35]- [37]. The IoT nodes K(x) are associated to available channels H. Each node k is associated to a cluster m. Each cluster m has different number of sensors as per the simulation parameters and that has semblance with packing in MKCP.…”

“…The instance (24a) being a sub-problem of (23a) can be categorized as MKCP, which is infact the instance of ordinary knapsack problem. Thus, foregone in view the problem (23a) is NP-hard as MKCP is NP-hard, [35]- [37].…”

Energy-Efficient Resource Allocation for NOMA-Based Heterogeneous 5G Mine Internet of Things

“…Zhang et al [22] investigated the resource optimization problem of NOMA heterogeneous small cell networks with simultaneous wireless information and power transfer (SWIPT), and proposed a subchannel matching algorithm and a power optimization algorithm based on the Langrangian duality method. In [23], the authors proposed optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNet. To maximize the energy efficiency (EE) subject to the minimum SE requirements and maximum transmission powers, an iterative algorithm with fast convergence speed and a reduced complexity searchbased algorithm are respectively presented to solve the power control and user selection problems [24].…”