Joint energy efficient power and subchannel allocation for uplink MC‐NOMA networks

Abstract

SummaryDue to the exponential rise in the number of subscribers in existing wireless networks, energy‐efficient distribution and utilization of network resources have become the preliminary objective of researchers nowadays. Nonorthogonal multiple access (NOMA) has attained widespread significance in this regard and has become a strong candidate for enhancing energy efficiency (EE) performance of existing networks. Multicarrier NOMA (MC‐NOMA) technique is an extension of NOMA that breaks the available resource block into various subchannels and allocates them efficiently to NOMA users (NUs). MC‐NOMA networks have the ability to further enhance system performance by efficiently exploiting channel diversity for addressing the problems of spectral inefficiency and power limitations. Therefore, in this paper, we have considered MC‐NOMA technology for an uplink scenario to investigate its performance for enhancing system EE by performing energy‐efficient power and subchannel allocation. To this end, we have formulated a joint user clustering, subchannel allocation, and power allocation problem for EE maximization (JSPEE) of uplink MC‐NOMA scenario. Due to the nonconvex, combinatorial nature of the problem, we propose a two‐step solution: that is, for each subchannel, first, subchannel allocation and user clustering are attained through low‐complexity suboptimal algorithm, which is followed by energy‐efficient power allocation of NUs. For subchannel allocation and user clustering, we exploit the channel diversity in the form of difference in channel gain values of various users. For power allocation, the nonconvex nature of the formulated problem is tackled by employing Dinkelbach and successive convex approximation (SCA) techniques to attain an energy‐efficient solution. Our simulation results portray that the JSPEE algorithm clearly outperforms the current NOMA as well as OMA works and enhances the system EE by efficient user clustering and exploiting channel diversity.