The Internet of Things (IoT) is a new network that connects massive devices which have the communication ability. With the requirement of various services and the wireless spectrums becoming increasingly scarce, we consider a nonorthogonal multicarrier transmission scheme that is spectrum efficient frequency division multiplexing (SEFDM) as the downlink transmission scheme for IoT network. SEFDM has the merit of improved bandwidth usage efficiency, but the serious inter carrier interference (ICI) will be introduced by multiplexing overlapped carriers. Thus, the signal detection is challenged for recovering the signal which is suffered from ICI due to the loss of the orthogonality. In this paper, a low complexity detector based on quasi-orthogonality compensation (QOC) is proposed in the downlink receiver. And the complexity of the QOC detector is also analyzed. Moreover, a novel detector which joint QOC and fixed sphere decoding (FSD) algorithm is proposed. The bit error rate performances of QOC and QOC-FSD detectors are evaluated by numerical simulations. Numerical results show that the QOC and QOC-FSD detectors can achieve better performance than conventional iterative detection (ID) and ID-FSD, respectively. Furthermore, QOC-FSD detector performs a lower complexity than ID-FSD detector.

With the evolutional development of maritime networks, the explosive growth of maritime data has put forward elevated demands for the computing capabilities of maritime devices (MDs). Unmanned aerial vehicle (UAV) is able to alleviate the computing pressure of MDs by forwarding the computing tasks to the edge server on the coast. However, UAV relaying introduces a significant security challenge due to the vulnerability of line-of-sight (LoS) communication channels, which can be exploited for eavesdropping on computing tasks. In this paper, an efficient secure communication scheme is proposed for UAV-relay-assisted maritime mobile edge computing (MEC) with a flying eavesdropper. The secure computing capacity of MDs is maximized by jointly optimizing the transmit power, time slot allocation factor, computation optimization and UAV trajectory. Due to multi-variable coupling, the formulated optimization problem (OP) is non-convex. We first transform OP by introducing auxiliary variables. Then, the transformed OP is decomposed and solved in an iterative manner by applying block coordinate descent (BCD) and successive convex approximation (SCA). Numerical results show that the secure computing capability of the UAV-relay-assisted maritime MEC system of proposed secure communication scheme can be effectively improved compared with benchmarks.

In this article, we present an unmanned aerial vehicle (UAV)-aided ocean monitoring network for remote oceanic data collection, in which monitoring data are transmitted first from battery-powered underwater sensor nodes (USNs) to sea surface sink nodes (SNs) in a data collection cycle using underwater acoustic communication, and then a UAV hovering in air collects all the data from SNs and relays them to a ground base station via wireless communication links. Aiming at maximizing network lifetime, we model the resource allocation, USN-to-SN access, and SN-to-UAV access issues as a mixed-integer nonconvex optimization problem. To efficiently solve it, we decompose the optimization into two stages. The first stage is to minimize time consumption in an SN-to-UAV nonorthogonal multiple access process and we solve it by designing a UAV deployment scheme, a subchannel matching scheme, and a joint power and time allocation scheme, based on which, the second stage is to maximize the residual energies of USNs in USN-to-SN transmissions under a modified frequency-division multiple access strategy in each collection cycle. The second-stage optimization is further decomposed into some similar subproblems, and each of them is considered as a bipartite graph matching problem between USNs and underwater acoustic channels. For each subproblem, we propose improved weight-based matching and bisection-based searching algorithms. Finally, we design a low-complexity iteration algorithm to approximate the optimal solution of the original problem by solving these subproblems. The simulation results validate the effectiveness of our proposals.

Fuel-powered UAVs have long endurance of flight, heavy payload and adaptation to extreme environment. The mechanical operation and communication power are supported independently by fuel and batteries. In the paper, we study the energy efficiency of the communication system with a fuel-powered UAV relay. We consider a three-node communication network, consisting of a mobile relay, a source node, and a destination node. The UAV relay is able to change its 3-D trajectory to maintain high probability of LoS channels, receiving information from the fixed source node and transmitting it to the fixed destination node. The power allocation scheme and UAV’s trajectory are designed to maximize the system energy efficiency, considering the constraints of speeds, UAV’s altitudes, communication and mechanical energy consumption, the required data rates of the destination node and information-causality. We solve the power allocation sub-problem by splitting the domain of variables and transforming it into a convex optimization problem. And then a suboptimal scheme is provided to design the trajectory based on successive convex approximation method. Numerical results show the convergence of the proposed schemes and the performance of the proposed algorithms. The influences of time slots, constraints of fuel, communication power and required data rates are discussed.

In this paper, we consider a single cell downlink non-orthogonal multiple access (NOMA) network and aim at maximizing the energy efficiency. The energy-efficient resource allocation problem is formulated as a non-convex and NP-hard problem. To decrease the computation complexity, we decouple the optimization problem as a subchannel matching scheme and power allocation subproblems. In the subchannel matching scheme, a non-cooperative game is applied to model this problem. To discuss the existence of Nash equilibrium (NE), we introduce a super-modular game and then design an algorithm to converge to the NE point. Moreover, a greed subchannel matching algorithm with low complexity is given through a two-way choice between users and subchannels. However, for given subchannel matching scheme, power allocation is still a non-convex problem, which is difficult to get the optimal solution. We then transform the non-convex problem to a convex problem by applying a successive convex approximation method. Afterward, we provide an algorithm to converge to suboptimal solution by solving a convex problem iteratively. Finally, simulation result demonstrates that the energy efficiency performance of the NOMA system is better than the orthogonal frequency division multiple access system.

This paper investigates the admission control problem on the satellite multi-beam networks with non-orthogonal multiple access (NOMA). The goal is to maximize the number of supported users on the premise of ensuring the quality of service (QoS) by optimizing the subchannel and power allocation. We provide the system model and then formulate the admission control problem as a mixed integer non-convex optimization problem. The non-convexity and existence of integer variable make the optimal solution difficult to get. Therefore, we propose a joint subchannel matching and power allocation algorithm to obtain the suboptimal solution so as to reduce the computation complexity. The proposed algorithm can be used for both NOMA and orthogonal frequency division multiplexing access (OFDMA). Specifically, the subchannel matching problem is solved by a two-stage matching process where users are accessed to subchannel dynamically. The power allocation problem is modeled as a super-modular game where the existence and uniqueness of Nash equilibrium (NE) are analyzed. Moreover, an iterative power allocation algorithm is proposed based on the NE searching method. Finally, simulation results are provided for demonstrating the effectiveness and feasibility of the proposed algorithm.