Nonorthogonal multiple access (NOMA) has been envisaged as a potential candidate for the forthcoming 5G cellular networks and beyond 5G networks. The existing user clustering schemes in NOMA systems exploit the channel heterogeneity and channel diversity to partition the users into different clusters by grouping the same number of users to each cluster. Due to the constraint of having the fixed number of users in each cluster, the channel heterogeneity and diversity cannot be fully explored, which causes the existing user clustering scheme to perform poorly in terms of throughput performance. In this article, an efficient and dynamic clustering method termed adaptive user clustering (AUC), which flexibly group the users to different clusters based on their channel conditions regardless of the cluster size, is proposed. The channel heterogeneity and diversity are fully exploited in user grouping that maximizes the system throughput. The clustering mechanism of the proposed AUC scheme is performed using the Brute‐force search (B‐FS) method by searching through all the possible partitions for the best partition with the highest throughput. Simulation results obtained demonstrate that the proposed AUC scheme using the B‐FS method always outperforms the existing user grouping approaches in various network scenarios in terms of throughput performance.

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Base station (BS) coordination with respect to data and energy cooperation has recently emerged as a potential solution for enhancing the energy efficiency (EE) of multi-cell multi-tier cellular network architecture. This work studies the EE maximization problem in a hybrid-powered (grid and renewable energy source) heterogeneous network (HetNet) where the data and energy are jointly coordinated among the BSs. We propose a combinatorial optimization algorithm to maximize the system EE with the aim to reduce grid power consumption (GPC). Due to the complexity of the formulation, Lagrange dual decomposition and metaheuristic method are incorporated to solve the problem. Furthermore, the nonfractional programming EE problem is solved using the Dinkelbach's method which converges faster with a lower complexity. Simulation results show that cooperation among the BSs to share the channel information and energy reduces the GPC by nearly 20% and increases EE around 10% during harvested energy scarcity among the BSs.

Non-orthogonal multiple access (NOMA) has gained considerable interest from the 3GPP community as a potential radio access strategy for the future fifth-generation (5G) wireless networks. Compared to orthogonal multiple access (OMA), NOMA is more efficient from the perspective of throughput performance making it more favorable for 5G systems. Existing NOMA techniques merely offer a rigid user grouping without exploring channel heterogeneity and diversity to cluster users, resulting in a poor throughput performance. An adaptive user clustering (AUC) approach has been proposed to search through all possible combinations to obtain the best clusters with the highest throughput. This scheme exploits the channel diversity of users to maximize throughput, however, the brute-force search (B-FS) method to find the optimal combinations results in a prohibitive complexity. In this paper, a novel artificial neural network (ANN) approach is proposed for user clustering in the downlink of the 5G NOMA system in order to maximize throughput performance at an acceptable complexity. In the proposed strategy, ANN model is first trained with the historical dataset, which contains the transmitting powers and channel gains of the downlink NOMA users, along with the information of the corresponding clusters which maximize the throughput performance of the system. Next, validation is performed to tune the values of hyper-parameters such as learning rate, length of training data, and epoch learned during training to validate cluster formation and to avoid over-fitting of the model. Finally, the ANN model is tested with the learned parameters and tuned hyper-parameters, to predict the formation of clusters and to evaluate the accuracy of the model. Simulation results demonstrate that the proposed scheme is able to obtain a significant reduction in terms of complexity with a performance of 98% for throughput (near-optimal throughput performance) when compared with the optimal approaches.

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Summary The new International Telecommunication Union Telecommunication G.fast standard for next‐generation ultra‐broadband digital subscriber line systems has stipulated mandatory use of vectoring for crosstalk mitigation. In this paper, we study the performance of downstream precoders in G.fast networks utilising the extended bandwidth up to 212 MHz. In particular, the issue of strong crosstalk encountered in practical copper cable bundles at high frequencies giving rise to channel matrices which are not row‐wise diagonally dominant (RWDD) as predicted theoretically, as well as the ramification of deploying precoders designed based on the RWDD assumption in practical G.fast channels are studied. To serve these purposes, an enhanced stochastic far‐end crosstalk (FEXT) model which provides distinct FEXT transfer functions with the dispersion range characterised experimentally whilst encompassing the dual‐slope FEXT encountered in non‐RWDD cables is used for evaluating the performance of precoders in diverse G.fast deployment scenarios. Furthermore, the performance sensitivity of precoders under realistic amount of channel estimation errors is investigated on a common framework, allowing a direct and fair comparison between linear and non‐linear precoders. Results show that the nonlinear optimal precoder outperforms linear precoders in all channel conditions; however, the former is found to be slightly more sensitive to channel estimation errors on shorter loops. Copyright © 2016 John Wiley & Sons, Ltd.