Optimal Resource Allocation for Full-Duplex IoT Systems Underlaying Cellular Networks with Mutual SIC NOMA

Device-to-device (D2D) and non-orthogonal multiple access (NOMA) are promising technologies to meet the challenges of the next generations of mobile communications in terms of network density and diversity for internet of things (IoT) services. This paper tackles the problem of maximizing the D2D sum-throughput in an IoT system underlaying a cellular network, through optimal channel and power allocation. NOMA is used to manage the interference between cellular users and full-duplex (FD) IoT devices. To this aim, mutual successive interference cancellation (SIC) conditions are identified to allow simultaneously the removal of the D2D devices interference at the level of the base station and the removal of the cellular users (CU) interference at the level of D2D devices. To optimally solve the joint channel and power allocation (PA) problem, a time-efficient solution of the PA problem in the FD context is elaborated. By means of graphical representation, the complex non-convex PA problem is efficiently solved in constant time complexity. This enables the global optimal resolution by successively solving the separate PA and channel assignment problems. The performance of the proposed strategy is compared against the classical state-of-the-art FD and HD scenarios, where SIC is not applied between CUs and IoT devices. The results show that important gains can be achieved by applying mutual SIC NOMA in the IoT-cellular context, in either HD or FD scenarios.

Resource Allocation in NOMA-based Self-Organizing Networks using Stochastic Multi-Armed Bandits

To achieve high data rates and better connectivity in future communication networks, the deployment of different types of access points (APs) is underway. In order to limit human intervention and reduce costs, the APs are expected to be equipped with self-organizing capabilities. Moreover, due to the spectrum crunch, frequency reuse among the deployed APs is inevitable, aggravating the problem of inter-cell interference (ICI). Therefore, ICI mitigation in self-organizing networks (SONs) is commonly identified as a key radio resource management mechanism to enhance performance in future communication networks. With the aim of reducing ICI in a SON, this paper proposes a novel solution for the uncoordinated channel and power allocation problems. Based on the multi-player multi-armed bandit (MAB) framework, the proposed technique does not require any communication or coordination between the APs. The case of varying channel rewards across APs is considered. In contrast to previous work on channel allocation using the MAB framework, APs are permitted to choose multiple channels for transmission. Moreover, non-orthogonal multiple access (NOMA) is used to allow multiple APs to access each channel simultaneously. This results in an MAB model with varying channel rewards, multiple plays and non-zero reward on collision. The proposed algorithm has an expected regret in the order of O(log^2 T ), which is validated by simulation results. Extensive numerical results also reveal that the proposed technique significantly outperforms the well-known upper confidence bound (UCB) algorithm, by achieving more than a twofold increase in the energy efficiency.