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An adaptive multicast scheme is proposed to overcome the spectral inefficiency problem of OFDMA (orthogonal frequency-division multiple access)-based multicast networks in the presence of high link quality differences among multicast users. The proposed scheme divides a multicast group into smaller sub-groups and subcarriers are allocated to maximise the aggregate data rate. Having smaller multicast groups allows multiuser diversity to be exploited more efficiently. Simulation results show that the proposed scheme can achieve nearoptimal performance whilst outperforming the conventional unicast and multicast schemes.Introduction: Recently, orthogonal frequency-division multiple access (OFDMA) has been widely studied for multicast systems owing to its great flexibility in spectrum management among multicast users [1][2][3]. In OFDMA-based multicast systems, provisioning of high-quality multimedia services to large numbers of subscribers, possibly located over a vast geographical area, requires an efficient multicast scheme. Since users belonging to the same multicast group are distributed at different locations, they experience different fading and path losses in the time-varying wireless channel. This presents a challenge to providing satisfactory multicast services to all users. The attainable data rate of each multicast stream is usually restricted by the data rate of the user with the worst channel condition. This results in poor efficiency when most users except a few are in good channel conditions and capable of delivering high rate transmissions. The multicast schemes studied in [1-3] have low system resource utilisation rates because they use conservative data rates to assure reliable multicast transmissions.This Letter addresses the aforementioned inefficiency problem of multicast communications in the presence of high link quality differences among users within a multicast group. Instead of transmitting the same copy of data to a multicast group at a very low bit rate via a single transmission, it could be more desirable to transmit multiple copies of the same data to different smaller sub-groups at higher rates because having smaller multicast sub-groups allows multiuser diversity to be exploited more efficiently. Therefore, we propose an adaptive multicast scheme which partitions a multicast group into smaller sub-groups and subcarriers are allocated to maximise the aggregate data rate (ADR). It is shown that, with the same number of subcarriers and available transmission power, the ADR can be increased considerably by using the proposed scheme over the conventional unicast scheme (CUS) and the conventional multicast scheme (CMS) [1].

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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.

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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In wireless ad hoc networks, co-channel interference can be suppressed effectively through proper integration of channel assignment (CA) and power control (PC) techniques. Unlike centralised cellular networks where CA and PC can be coordinated by base stations, the integration of CA and PC into infrastructureless wireless ad hoc networks where no global information is available is more technically challenging. The authors model the CA and PC problems as a non-cooperative game, in which all wireless users jointly pick an optimal channel and power level to minimise a joint cost function. To prove the existence and uniqueness of Nash equilibrium (NE) in the proposed non-cooperative CA and PC game (NCPG), the authors break the NCPG into a CA subgame and a PC subgame. It is shown that if NE exists in these two subgames, the existence of NE in the NCPG is ensured. Nonetheless, due to unpredictable network topology and diverse system conditions in wireless ad hoc networks, the NCPG may encounter the 'ping-pong' effect that renders NE unattainable. By incorporating a call-dropping strategy and no-internal-regret learning into the NCPG, an iterative and distributed algorithm that ensures convergence to NE is proposed. It is shown through simulation results that the proposed approach leads to convergence and results in significant improvements in power preservation and system capacity as compared with the popular distributed dynamic CA technique incorporated with PC.