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Fifth‐generation (5G) and future beyond fifth‐generation (B5G) networks will require immense capacity due to the high rise in the number of multimedia applications and mobile devices. The heterogeneous networks (HetNets) in 5G and B5G can increase the heterogeneity and network throughput because macro base station‐only (MBS‐only) networks cannot satisfy the drastic increase in capacity demands. Hybrid nonorthogonal multiple access (H‐NOMA) can accommodate the increasing number of multimedia applications and mobile devices in 5G and B5G. H‐NOMA in B5G can also improve spectral efficiency. Before this, the researchers had not considered user clustering with H‐NOMA in HetNets. This article investigates the user clustering with downlink H‐NOMA in HetNet to maximize the network throughput in 5G and B5G. The formulated mathematical problem optimizes the considered key performance indicators (KPIs), that is, users admission in clusters, users association with base stations, power allocation to users, and network throughput. At the same time, it also satisfies the minimum transmit power and data rate requirements of users. The formulated problem is a mixed‐integer nonlinear programming (MINLP) problem. We have proposed an ϵ$$ \epsilon $$‐optimal algorithm, that is, outer approximation algorithm (OAA) to solve the MINLP problem because the complexity of the optimal exhaustive search algorithm (ESA) increases exponentially with an increase in the number of users. H‐NOMA with user clustering in HetNet is evaluated with extensive simulations to show its effectiveness regarding network throughput in 5G and B5G. We have also considered the proposed framework in the MBS‐only network and compared its performance with HetNet. The results verify that the proposed framework in HetNet performs better than the MBS‐only network. The complexity of the ϵ$$ \epsilon $$‐optimal algorithm is calculated that gives ϵ$$ \epsilon $$‐optimal results within ϵ=10prefix−3$$ \epsilon =1{0}^{-3} $$. We have also made a complexity analysis of the proposed OAA and ESA and concluded that the complexity of the proposed OAA algorithm is less than the ESA.

The resource allocation solution offered based on Non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes are sub-optimal to address the challenging quality of service (QoS) and higher data rate viz-a-viz energy efficiency (EE) requirements in 5th generation (5G) cellular networks. In this work, we maximize the EE using user equipment (UE) clustering (UE-C) with downlink hybrid NOMA (H-NOMA) assisted beyond 5G (B5G) HetNets. We formulate an optimization problem incorporating UE admission in a cluster, UE association with a base station (BS), and power allocation assisted by H-NOMA, i.e., OMA and NOMA schemes in the macro base station (MBS) only and heterogeneous networks (HetNets) environments. The problem formulated is a type of non-linear concave fractional programming (CFP) problem. The Charnes-Cooper transformation (CCT) is applied to the formulated non-linear CFP problem to convert it into a concave optimization, i.e., mixed-integer nonlinear programming (MINLP) problem. A two-phase ϵ-optimal outer approximation algorithm (OAA) is used to solve the MINLP problem. The simulation results show that H-NOMA with HetNets outperforms H-NOMA with MBS only in terms of UE admission, UE association, throughput, and EE. INDEX TERMS UE-clustering, H-NOMA, fractional programming, MINLP, energy efficiency I. INTRODUCTIONT HE rapid growth in the number of mobile user equipment (UE), and heavy data-driven applications, i.e., live video gaming, video streaming, social networking, etc are imposing challenging requirements like minimum delay, higher data rates, spectrum efficiency (SE), and energy efficiency (EE) on the beyond 5th Generation (B5G) cellular networks. This exponential growth in mobile UEs viz-aviz mobile data traffic is adding to a significant increase in the energy consumption in cellular networks. Information communication technology (ICT) is consuming almost 2% of the world's total energy. Energy consumption emits carbon which causes the greenhouse effect. Thus, ICT offering higher data rates, low carbon emission, and EE are the prime considerations in B5G cellular networks. Thus, academia and industry in the wireless communication field must divert their research towards future energy-efficient green cellular networks in B5G networks [1]-[3]. The abbreviations used in this work are defined in Table A1 of Appendix A.Energy-efficient radio resource management techniques are required to satisfy the needs for quality of service (QoS) and higher data rates with minimum energy consumption. EE is the ratio of data rate to the total energy consumed [4], [5].EE can be improved using heterogeneous networks (Het-Nets), which include a macro base station (MBS) and small base stations (SBSs) [6]- [8]. HetNets cover more geographical areas and offer higher data rates and are energy efficient than the conventional MBS-only networks. In HetNets, MBS is large with high transmit power, and SBSs (i.e., femtocell, picocell, etc.) are of small size with low transmit power [9]. A low transmit-powered SBS...

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