The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. The sum rate is maximized by jointly optimizing the UAV trajectory and the NOMA precoding. To solve the optimization, we decompose it into two steps. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory, with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained using two schemes with different constraints. The first scheme intends to cancel the interference from the BS to the UAV-served user, while the second one restricts the interference to a given threshold. In both schemes, non-convex optimization problems are converted into tractable ones. An iterative algorithm is designed. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.

In this paper, we consider a reconfigurable intelligent surface (RIS) assisted unmanned aerial vehicle (UAV) relaying communication system, where the RIS is mounted on the UAV and can move at a high speed. Compared with the conventional static RIS, better performance and more flexibility can be achieved with the assistance of the mobile UAV. We maximize the average downlink throughput by jointly optimizing the UAV trajectory, RIS passive beamforming and source power allocation for each time slot. The formulated non-convex optimization problem is decomposed into three subproblems: passive beamforming optimization, trajectory optimization and power allocation optimization. An alternating iterative optimization algorithm of the three subproblems is proposed to achieve the suboptimal solutions. The numerical results indicate that the RIS-UAV relaying communication system with trajectory optimization can get higher throughput.

Multibeam satellite systems (MSS) enable transmission flexibility and spatial diversity while efficiently reusing the scarce spectrum resource. However, as spectrum reuses tend to introduce co-channel interference, MSS need to address power allocation and interference management carefully. In this paper, we tackle the joint interference pricing and power allocation problems of MSS by formulating the underlying resource allocation problem as a dynamic game model - the Stackelberg model. In our proposed scheme, a fresh satellite user will be charged according to its interference on the satellite system. MSS can dynamically adjust the interference price in order to make a tradeoff between intercell interference and operating profit. Meanwhile, for the satellite user, an equilibrium power allocation should be ascertained in response to the MSS's pricing. A novel market-based solution is proposed for interference management in MSS by introducing an elastic price mechanism. The Nash equilibrium for interference pricing and its iterative convergence for power allocation have further been proven. Numerical results are provided to evaluate the impact of different prices on the utility functions of both MSS and satellite users.

The evolution of IoT devices has generated huge demand for wireless bandwidth in order to meet the operational needs of new generation IoT applications. This bandwidth problem is further exacerbated by another characteristics of IoT applications, i.e. IoT devices are usually deployed in massive number, thus leading to an awkward scenario that many bandwidth-hungry devices are chasing after the very limited wireless bandwidth within a small geographic area. As such, cognitive radio has received much attention of the research community as an important means for addressing the bandwidth needs of IoT applications. In this paper, we investigate the structure frameworks and potential applications of cognitive IoT. We further discuss the spectrum-based functionalities and heterogeneity for cognitive IoT. Security and privacy issues involved in cognitive IoT are also investigated. Finally, the research challenges and future direction for the cognitive-radio-based IoT networks are presented.

This paper is concerned with the matching stability problem across different decoder layers in DEtection TRansformers (DETR). We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR. To address this problem, we show that the most important design is to use and only use positional metrics (like IOU) to supervise classification scores of positive examples. Under the principle, we propose two simple yet effective modifications by integrating positional metrics to DETR’s classification loss and matching cost, named position-supervised loss and position-modulated cost. We verify our methods on several DETR variants. Our methods show consistent improvements over baselines. By integrating our methods with DINO, we achieve 50.4 and 51.5 AP on the COCO detection benchmark using ResNet-50 backbones under 1× (12 epochs) and 2× (24 epochs) training settings, achieving a new record under the same setting. We achieve 63.8 AP on COCO detection test-dev with a Swin-Large backbone. Our code will be made available at https://github.com/IDEA-Research/Stable-DINO.

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework.