Modern 5G heterogeneous networks (HetNets) require hybrid multiple access technology for optimal performance. The feasibility of a hybrid power domain sparse code nonorthogonal multiple access (PD‐SCMA) that integrates both power domain nonorthogonal multiple access (PD‐NOMA) and sparse code multiple access (SCMA) for an uplink hierarchical HetNet system is demonstrated. Hybrid schemes namely: Successive Codebook Ordering Assignment (SCOA) for codebook assignment (CA), opportunistic MUE‐SUE pairing (OMSP) for user pairing (UP), and a QoS‐aware power allocation (QAPA) for power allocation (PA) are developed. The SCOA algorithm is based on channel quality ordering metric, OMSP algorithm is based on channel quality diversity and pairing interference metric while the QAPA algorithm features a QoS awareness metric. A joint energy efficiency (EE) resource allocation (JEERA) algorithm that iteratively performs CA, UP, and PA for small cell user equipment (SUE) and the macro user equipment (MUE) to limit interference, improve spectral and energy efficiency is presented. The problem is formulated as a mixed integer nonconvex system EE resource allocation optimization for the small cells under QoS constraints of minimum sum‐rate, interference temperature, maximum power, and SCMA structure for a hybrid low complexity joint SIC‐Log‐MPA receiver. A modified near‐optimal dual decomposition analytical methodology featuring Dinkelbach fractional transformations is utilized to assess the system's performance on an imperfect wireless channel. Through numerical results, the proposed schemes are shown to improve the EE of the small cells in comparison with the prevalent schemes.

This work presents a combined energy-efficient medium access control (MAC) and routing protocol for large-scale wireless sensor networks that aims to minimise energy consumption and prolong the network lifetime. The proposed communication framework employs the following measures to enhance the network energy efficiency. Firstly, it provides an adaptive intra-cluster schedule to arbitrate media access of sensor nodes within a cluster, minimising idle listening on sensor nodes, leading to improved energy performance. Secondly, it proposes an on-demand source cross-layer routing protocol ensuring selection of best routes based on energy level and channel quality indicator for the multihop inter-cluster data transmission. Lastly, an unequal cluster size technique based on cluster head residual energy and distance away from the base station is utilised. This technique balances the energy among clusters and avoids early network partitioning. This work further presents the analytical performance model for energy consumption and delay of the proposed communication framework. The performance measures used for evaluation are energy consumption, delay, and network lifetime. The results indicate that combining routing and MAC schemes conserves energy better than utilising MAC scheme alone.

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Due to their ability to multiplex users on a resource element (RE), Non-orthogonal multiple access (NOMA) techniques have gained popularity in 5G network implementation. The features of 5G heterogeneous networks have necessitated the development of hybrid NOMA schemes combining the merits of the individual NOMA schemes for optimal performance. The hybrid technologies on 5G networks make complex air interfaces resulting in new resource allocation (RA) and user pairing (UP) challenges aimed at limiting the multiplexed users interference. Furthermore, common analytical techniques for evaluating the performance of the schemes lead to unrealistic network performance bounds necessitating alternative schemes. This work explores the feasibility of a hybrid power domain sparse code non-orthogonal multiple access (PD-SCMA). The scheme integrates both power and code domain multiple access on an uplink network of small cell user equipments (SUEs) and macro cell user equipments (MUEs). Alternative biological RA/UP schemes; the ant colony optimization (ACO), particle swarm optimization (PSO) and a hybrid adaptive particle swarm optimization (APASO) algorithms, are proposed. The performance results indicate the developed APASO outperforming both the PSO and ACO in sum rate and energy efficiency optimization on application to the PD-SCMA based heterogeneous network.

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