Non-orthogonal multiple access (NOMA) holds the promise to be a key enabler of 5G communication. However, the existing design of NOMA systems must be optimized to achieve maximum rate while using minimum transmit power. To do so, this paper provides a novel technique based on multi-objective optimization to efficiently allocate resources in the multi-user NOMA systems supporting downlink transmission. Specifically, our unique optimization technique jointly improves spectrum and energy efficiency while satisfying the constraints on users quality of services (QoS) requirements, transmit power budget and successive interference cancellation. We first formulate a joint problem for spectrum and energy optimization and then employ dual decomposition technique to obtain an efficient solution. For the sake of comparison, a low complexity single-objective NOMA optimization scheme is also provided as a benchmark scheme. The simulation results show that the proposed joint approach not only performs better than the traditional benchmark NOMA scheme but also significantly outperforms its counterpart orthogonal multiple access (OMA) schemes in terms of both energy and spectral efficiency.

Recently, non‐orthogonal multiple access (NOMA) has been considered as a promising radio access technique for the upcoming fifth generation networks. In this study, the authors consider the downlink multi‐cell multi‐user cellular network, where the base station (BS) located in the centre of each cell intends to communicate with multiple random distributed user equipments (UEs) using NOMA protocol. They formulate an optimisation problem to increase the sum network capacity under various practical constraints. Especially, the transmission power budgets for BSs, power allocation for UEs, and minimum rate requirements per UE are optimised. Moreover, they obtain the optimal power allocation via local optimal solution iteratively, which satisfies Karush–Kuhn–Tucker conditions. The simulation results show that the proposed scheme can converge after few iterations and achieve higher sum capacity compared with the non‐optimal NOMA and orthogonal multiple access schemes.

The future generation of wireless communication networks demands for high spectral efficiency to accommodate a large number of devices over the limited available frequency spectrum. Device to device (D2D) systems exploit channel reuse to offer high spectral efficiency and reduce the burden on the communication infrastructure by facilitating communication between devices without involving the base station. We can further enhance the efficiency of D2D systems by employing non-orthogonal multiple access (NOMA) for the transmission of the signals. In NOMA the signals of multiple users are transmitted on the same channel, simultaneously. Deployment of relays can assist the users that do not have a reliable link of communication. A combination of these advanced technologies may offer very high spectral efficiency and a robust communication system. This article aims to design efficient resource allocation techniques for the future communication systems. We consider sum rate maximization problem subject to limited power budget at different transmitting nodes and necessary transmit power gap among users for successful NOMA implementation. Under decode and forward relaying protocol, the problem turns out to be a unique joint uplink-downlink NOMA optimization. We then propose a deep neural networks (DNN) framework to acquire a joint power loading solution at source and relaying nodes. To obtain reliable data for DNN training and testing, we also derive an optimal solution of the problem through convex optimization paradigm, which is used later as a bench mark to verify the performance of proposed DNN based solution. It is observed that DNN provides promising results both in terms of sum rate and the computational complexity.

In this paper, we present a joint power allocation and adaptive link selection protocol for an orthogonal frequency division multiplexing (OFDM)-based network consists of one source node i.e., base station (BS), one destination node i.e., (MU) and a buffer aided decode and forward (DF) relay node. Our objective is to maximize the average throughput of the system via power loading over different subcarriers at source and relay nodes. A separate power budget is assumed at each transmitting node to make the system more practical. In order to form our solution more tractable, a decomposition framework is implemented to solve the mixed integer optimization problem. Further, less complex suboptimal approaches have also been presented and simulation results are provided to endorse the efficiency of our designed algorithms.

Vehicular communication is gradually evolving from the rudimentary data exchange techniques to innovative, efficient, and large-scale communication platform. The future vehicular networks aim to offer new services and achieve massive connectivity with the aid of advanced wireless communication techniques. In this regard, one of the emerging paradigms is non-orthogonal multiple access (NOMA) which holds the promise to meet the resource-intensive demands of future networks. Due to this reason, the applications of NOMA are being investigated by the researcher of academia and industry alike. This article aims to build on the state-of-the-art by providing a novel multiobjective optimization framework for vehicle-to-infrastructure (V2I) communications. Specifically, this work aims to improve upon the sum-rate and energy efficiency of uplink NOMA-enabled V2I communications. The formulated problem is based on the tradeoff between sum-rate and total transmit power while maintaining the quality of service (QoS) requirements of the network. To provide a reliable solution, a multiobjective optimization framework has been proposed by applying the weighted-sum method and handle joint metrics of sum-rate maximization and total transmit power minimization. Extensive simulations have been performed to validate the superiority of the proposed framework against conventional benchmark techniques. The results clearly indicate that the proposed framework is feasible for NOMA-enabled V2I communication and can be scaled up for practical implementation.