Yue Li

Spectrum sensing is a key technology to detect unused frequency bands, and is widely applied in spectrum sharing and dynamic channel allocation. However, it is a challenge to provide high sensing accuracy under low signal-to-noise-ratio (SNR) environments. To address this issue, this paper proposes a novel method based on feature extraction and fusion clustering. First, the sampling matrix of the received signal is decomposed into two orthogonal components I and Q, and Cholesky decomposition is performed on the covariance matrices of I and Q components to extract their two-dimensional feature vectors. Then, the fusion clustering algorithm is proposed, where the GMM clustering algorithm is performed to classify the feature vectors, and the initial parameters of GMM, such as centroids, weights and covariance matrices, are generated by K-means clustering. Simulation results show that the proposed method accelerates the convergence speed of GMM and improves the classification accuracy. It effectively enhances the performance of spectrum sensing compared to other mainstream methods.

In indoor low-light environments, the lack of light makes the captured images often suffer from quality degradation problems, including missing features in dark areas, noise interference, low brightness, and low contrast. Therefore, the feature extraction algorithms are unable to extract the feature information contained in the images accurately, thereby hindering the subsequent target search task in this environment and making it difficult to determine the location information of the target. Aiming at this problem, a joint local and high-level semantic information (JLHS) target search method is proposed based on joint bilateral filtering and camera response model (JBCRM) image preprocessing enhancement. The JBCRM method improves the image quality by highlighting the dark region features and removing the noise interference in order to solve the problem of the difficult extraction of feature points in low-light images, thus providing better visual data for subsequent target search tasks. The JLHS method increases the feature matching accuracy between the target image and the offline database image by combining local and high-level semantic information to characterize the image content, thereby boosting the accuracy of the target search. Experiments show that, compared with the existing image-enhancement methods, the PSNR of the JBCRM method is increased by 34.24% at the highest and 2.61% at the lowest. The SSIM increased by 63.64% at most and increased by 12.50% at least. The Laplacian operator increased by 54.47% at most and 3.49% at least. When the mainstream feature extraction techniques, SIFT, ORB, AKAZE, and BRISK, are utilized, the number of feature points in the JBCRM-enhanced images are improved by a minimum of 20.51% and a maximum of 303.44% over the original low-light images. Compared with other target search methods, the average search error of the JLHS method is only 9.8 cm, which is 91.90% lower than the histogram-based search method. Meanwhile, the average search error is reduced by 18.33% compared to the VGG16-based target search method. As a result, the method proposed in this paper significantly improves the accuracy of the target search in low-light environments, thus broadening the application scenarios of target search in indoor environments, and providing an effective solution for accurately determining the location of the target in geospatial space.

To address the need for massive connections in Internet-of-Vehicle communications, local wireless networks utilize non-orthogonal multiple access (NOMA). Scholars have introduced deep reinforcement learning networks for user grouping and power allocation to reduce computational complexity. However, the traditional algorithm based on DQN (Deep Q-Network) still exhibits slow convergence speed and low training stability, while the uniform sampling method in the sample playback process suffers from low sampling efficiency. In order to address these issues, this paper proposes a user grouping and power allocation method for NOMA systems based on Prioritized Dueling DQN-DDPG joint optimization. Firstly, the paper introduces the user grouping network based on Dueling DQN, which considers both the state value and action value in the entire connection layer. The two values compete with each other, are summed up, and re-evaluated. The network significantly improves training stability and increases the convergence speed. Secondly, in this paper, a depth deterministic strategy gradient (DDPG) algorithm with symmetric properties is used. This algorithm works well for continuous action spaces and avoids the power quantization error because of the continuity of power value in the power allocation stage. Finally, the priority sampling based on TD-error (Temporal-difference error) is combined with the Dueling DQN network and DDPG network to ensure random sampling and improve the replay probability of important samples. Simulation results show that the proposed priority-based Dueling DQN-DDPG algorithm significantly improves the convergence speed of sample training. The research results of this paper provide a solid foundation for the following research content, which focuses on NOMA system resource allocation under the mobile user state.

In the mobile communication system, non-orthogonal multiple access (NOMA) technology is introduced to improve spectrum efficiency. Because the combination of users and the transmission power of each user are very important to the performance of NOMA system, the resource allocation technology of NOMA system has been widely studied. In recent years, scholars have introduced deep reinforcement learning network for user grouping and power allocation, which can effectively reduce the computational complexity and improve the system sum rate. However, the traditional algorithm based on DQN network still has the problems of slow convergence speed and low training stability, and the uniform sampling method in the sample playback process has the problem of low sampling efficiency. To address these problems, this paper proposes a priority-based user grouping and power allocation method of NOMA system optimized by Dueling DQN-DDPG, which can effectively improve the convergence speed and training stability. Firstly, in the user grouping stage, a user grouping network based on Dueling DQN is proposed. This network considers both the state value and the action value in the whole connection layer. The two values compete with each other, and then they are summed up and re-evaluated. The proposed network can effectively improve the stability of the traditional DQN network training process and speed up the training convergence. Secondly, considering the continuity of power value, DDPG network, which is suitable for dealing with continuous action space, is adopted in the power allocation stage, which can avoid the power quantization error. Finally, the priority sampling based on TD-error is combined with Dueling DQN network and DDPG network respectively, which can ensure random sampling and improve the replay probability of important samples. Simulation results show that the priority based Dueling DQN -DDPG algorithm proposed in this paper can greatly improve the convergence speed of sample training. At the same time, this scheme has the advantage of priority sampling, which can improve the learning speed and make the training process more stable. Compared with the traditional DQN algorithm, the convergence speed of the proposed algorithm is nearly doubled, and the training process is more stable, but the computational complexity is only increased by about 15%.

To meet the demands of massive connections in the Internet-of-vehicle communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning network is utilized for resource optimization in NOMA system, where a uniform sampled experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. To this point, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the system sum rate. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. Although the sum rate is improved by only 2%, the computational complexity is reduced by 43% and 64% compared to the exhaustive search algorithm and the optimal iterative power optimization algorithm, respectively.

To meet the demands of massive connections in the Internet-of-vehicle (IoV) communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, power multiplexing and successive interference cancellation techniques are utilized at the transmitter and the receiver respectively to increase system capacity, and user grouping and power allocation are two key issues to ensure the performance enhancement. Various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning (DRL) network is utilized to solve the resource allocation problem. In a DRL network, an experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. Different from conventional methods, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the sum rate of the NOMA system. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed to solve the problem of random sampling, where TD-error is used to represent the priority of sample, and the DQN takes samples according to their priorities. In addition, sum tree is used to store the priority to speed up the searching process. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized to complete power allocation tasks for each user. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. While the computational complexity is reduced by 43% and 64% compared with the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively.