In this paper, we study resource allocation problems for a two-tier cognitive heterogeneous network in interweave spectrum sharing mode. Secondary users (SUs) in small cells (SCs) opportunistically access the licensed spectrum resources. Non-orthogonal multiple access (NOMA) is used to boost the number of accessible SUs sharing the limited and dynamic licensed spectrum holes. Practically, there exists a tradeoff: an SC can increase its instantaneous sum throughput by accessing more idle bandwidth, which creates higher liability due to the dynamics of licensed spectrum and contention among the multiple SCs. Aiming to maximize the sum throughput of second-tier SCs network, we formulate a mixed integer nonlinear programming problem with the constraints of the available idle bandwidth, the successive interference cancellation complexity, the transmission power budget, and the minimum data requirements. To efficiently solve this problem, we decompose the original optimization problem into bandwidth resource allocation subproblem, SUs clustering subproblem, and power allocation subproblem. Based on the scale of SCs network and the activities of licensed spectrum, we introduce an optimal bandwidth configuration to maximize the average sum throughput of SCs. By analyzing the derivation of the achievable rate expression of a NOMA-enabled SU, we develop a novel SUs clustering algorithm which can improve the throughput of a cluster by grouping SUs with more distinctive channel conditions. With the results of SUs clustering, we propose power allocation within a NOMA cluster by using Karush-Kuhn-Tucker optimality conditions. Furthermore, we perform power allocation across NOMA clusters by using the difference of convex programming. Simulation results validate the performance of the proposed resource allocation algorithms.

In this paper, we study the utility fairness resource allocation in multi-user orthogonalbased cognitive radio network (CRN) with cochannel interference (CCI) mitigation. In our proposed system model, we introduce the correct reception probability (CRP) model as a network utility metric. Furthermore, useful bounds on CRP are derived to analyze the performance of proposed allocation schemes. The optimal resource allocation is formulated as a worst-case user CRP maximum problem with both average CCI and average power budget constraints. However, this problem is non-convex and generally challenging to solve. Therefore, we solve this problem by successively performing subchannel allocation and power allocation. Firstly, a k-means clustering inspired subchannel allocation strategy is proposed to divide secondary users (SUs) into multiple groups by minimizing the average mutual-signal-to-interference-ratio degree between any two SUs. The concept of reference user is employed to guarantee the quality of service of the primary user (PU). In each subchannel, we formulate a max-min utility optimal power allocation problem. The nonlinear Perron Frobenius theory is applied to solve this power allocation problem. Simulation results show that the proposed resource allocation scheme is fair and has relatively fast convergence.

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

In this paper, an unmanned aerial vehicle is utilized as an aerial relay to connect onshore base station with offshore users in a maritime IoT system with uplink non-orthogonal multiple access enabled. A coordinated direct and relay transmission scheme is adopted in the proposed system, where close shore maritime users directly communicate with onshore BS and offshore maritime users need assistance of an aerial relay to communicate with onshore BS. We aim to minimize the total transmit energy of the aerial relay by jointly optimizing the UAV hovering position and transmit power allocation. The minimum rate requirements of maritime users and transmitters’ power budgets are considered. The formulated optimization problem is non-convex due to its non-convex constraints. Therefore, we introduce successive convex optimization and block coordinate descent to decompose the original problem into two subproblems, which are alternately solved to optimize the UAV energy consumption with satisfying the proposed constraints. Numerical results indicate that the proposed algorithm outperformed the benchmark algorithm, and shed light on the potential of exploiting the energy-limited aerial relay in IoT systems.

Recently, unmanned aerial vehicle (UAV)-assisted maritime communication systems have drawn considerable attention due to their potential for broadband maritime communication applications. However, their limited energy resources remains a critical issue in providing long-term data transmission support for maritime applications. In this study, an integrated sea–air–terrestrial communication system was constructed for marine data collection, where several unmanned surface vessels (USVs) are deployed to collect marine data from underwater sensors (UWSs), and a UAV hovers above these USVs as a relay node, transmitting marine data from USVs to an onshore base station (BS). To prolong the lifetime of the UAV relay, mobile edge computing technology is applied in USVs for partial data computing, which reduces the to-be-relayed data volume from UAVs to USVs to onshore BS as well as the relay energy consumption of UAV. A parallel data computing and transmission scheme was developed for simultaneous local data computing and relaying in the proposed system. Accordingly, a UAV energy consumption minimization problem was formulated with constraints on the USV’s computational ability, the USV’s transmission power budget, the UAV transmission power budget, and the maximum system latency. To effectively solve this nonconvex optimal problem, an energy optimal partial data computing and relaying strategy was constructed by successively optimizing the data partial computational offloading ratio, USV transmit power allocation, and UAV transmit power. Numerical simulations were used to verify the effectiveness of the proposed strategy.

In order to improve the secrecy performance, we propose a secure hybrid radio frequency/free space optical (RF/FSO) transmission scheme that takes advantage of both the RF and FSO channels to protect the privacy messages. In the proposed scheme, Alice adaptively selects RF link or FSO link for information transmission according to the secrecy performance of each link. Considering the priority of the FSO link, we propose two secure transmission policies: the FSO dominant secure (FDS) policy and the secrecy rate optimal (SRO) policy. In the FDS policy, we assign high priority to the FSO link due to its high secrecy performance. Therefore, in this policy, Alice transmits the privacy information through the FSO link when the FSO link is reliable. When the FSO link cannot provide successful transmission, Alice will consider the RF secure transmission. In the SRO policy, Alice optimally selects FSO link or RF link according to the secrecy rates of both the FSO and RF links in each time slot. For both FDS and SRO policies, we analyze the secrecy performances and derive closed-form expressions for the average secrecy rate. Numerical results demonstrate the performance improvement of the proposed policies when compared with the current RF or FSO secure schemes in terms of the average secrecy rate.