Cell-free massive Multiple Input Multiple Output (MIMO) systems, which can be applied to deploy a large number of geographically dispersed access points within an area, have been playing an essential role in the operation of next-generation mobile communication networks due to their better performance in the flexibility of network deployment. The key to improving network service is to choose the appropriate APs and effectively control their transmission power for reduced co-channel interference and improved overall performance. To address co-channel interference and reduce computational complexity, a pilot allocation-based access point selection algorithm is proposed in this study that is used to iteratively allocate pilots, with access points selected to significantly reduce pilot contamination and serve user. However, considering the complexity of max-min power control, the particle swarm optimization (PSO) algorithm is applied in this paper as the solution. To prevent the algorithm from falling into local optima, the PSO algorithm is further improved by adjusting the inertia weight flexibly, thereby making the algorithm more efficient in searching for the global optimum. Simulation results are obtained to validate the proposed algorithm, which significantly enhances the overall spectral efficiency of the users while ensuring a consistent quality of services delivered to the users.

Due to the impact of database expansion and fluctuations in Received Signal Strength (RSS), the accuracy of WIFI indoor fingerprinting localization has decreased. To address this issue, this paper proposes a WIFI indoor fingerprinting localization algorithm based on Global Search K-means Clustering and Modified Weighted K-nearest neighbors (GSKCMW). Specifically, in the offline phase, we propose a novel K-means clustering algorithm that utilizes an improved Particle Swarm Optimization (PSO) technique and dual distances between RSS indicators and location coordinates for clustering, thus avoiding the problem of converging to local minima. Subsequently, an improved Weighted K-nearest Neighbors (WKNN) online matching algorithm by incorporating distance weights is improved to enhance the Mahalanobis distance, approximating the true distance between reference points and test points, thereby refining the accuracy of nearest neighbor fingerprints. Additionally, we employ dynamic K-values in WKNN to enable different optimal neighbor selections for different test points at each location, further enhancing localization accuracy. Simulation results indicate a significant improvement in localization precision achieved by this algorithm.

Integrating cognitive radio into Internet of Things (IoT) is conducive to reducing spectrum scarcity for large-scale IoT deployment, where a core technology is the design of spectrum access algorithms for effective assignment of spectrum holes. However, due to the partially observable channels and increased number of users in the cognitive radio Internet of Things (CRIoT) network, the secondary users have difficulty avoiding interferences and accessing the spectrum quickly. This study presents a distributed dynamic spectrum access (DSA) algorithm that employs a priority experience replay deep echo state Q-network (PER-DESQN) for CRIoT networks with multiple users and channels. To accelerate the Q-network convergence, we use an echo state network based on the underlying temporal correlation to estimate Q-values. Then, to resolve the Q-value overestimation and improve prediction accuracy, the estimated Q-value and decision action process are trained using a double deep Q-network (DDQN). Moreover, a priority experience replay mechanism that uses the Sum-Tree combined with importance sampling weights is proposed to optimize the DDQN to address the instability of the Q-value resulting from random sampling. As the simulation results demonstrate, the proposed algorithm can make fast and accurate DSA decisions and boost the network channel capacity significantly.

Hybrid precoding has been recognized as promising and effective for practical 5G communication. It is generally challenging to obtain the sample for deep learning-based hybrid precoding due to its need of massive precoding vector and channel matrix. To effectively solve this issue, a novel coordinated hybrid precoding algorithm based on unsupervised learning graph attention networks (CHP-ULGAT) is first developed by making full use of the underlying topology formed by the channel matrix. Subsequently, a more realistic situation of existing an ultra-low execution time is considered. A sub-optimal coordinated hybrid precoding based on unsupervised learning convolutional neural networks (CHP-ULCNN) is proposed to further reduce complexity. Moreover, we present effective ways to design the multi-matrix operation and the loss function to address the practicability of the algorithm. Extensive simulation results show that the proposed hybrid-precoding algorithms have obvious advantages in spectral efficiency (SE) and energy efficiency (EE) improvement with ultra-low computational complexity, considering the different number of RF chains and deployment scenarios.

Terahertz (THz) massive multiple-input multiple-output (MIMO) is considered as a key technology for future sixth-generation (6G) wireless communications, in which hybrid precoding facilitates an important trade-off of hardware cost and spectrum efficiency. However, the performance of traditional schemes is limited owing to the beam split effect and the non-convex optimization problem as well as the inter-user interference under imperfect channel state information (CSI) in THz massive multi-user (MU)-MIMO systems. To overcome these challenging problems, we propose an unsupervised convolutional neural network (CNN)-based hybrid precoding scheme with attention mechanism. Specifically, we first adopt the true-time-delay (TTD) structure to mitigate beam splitting. Then, to solve the non-convex optimization problem of TTD hybrid precoding and to further mitigate inter-user interference, we propose a robust hybrid precoding scheme by applying the attention mechanism and CNN, which can be trained to generate an optimal analog precoder targeting at an achievable rate maximization under imperfect CSI. Simulation results show that the proposed algorithm has good robustness and can maintain excellent achievable rate performance in the case of imperfect CSI.

Millimeter wave (mmWave) communication combined with massive multiple input multiple output (MIMO) system is one of the most promising technologies in future wireless networks due to the characteristics of high bandwidth and narrow beam. For the strong channel attenuation, the more accuracy channel state information (CSI) is needed to make sure that the signal is received accurately in mmWave system. In this paper, a hardware-friendly channel estimation algorithm, named modular image denoising approximation message passing (MIDAMP), is proposed by combining the real image denoising network (RIDNet) and the learning approximation message passing network. The gap between the estimated channel and the real channel can be greatly reduced through using the powerful denoising ability of MIDAMP. The results of simulation demonstrate that the proposed MIDAMP algorithm has better performance in estimation accuracy and achievable sum rate (ASR) compared with some existing algorithms.

Indoor positioning technology, as a crucial foundation of location-based services, is experiencing a growing need for high precision driven by the Internet of Things (IoT). However, traditional positioning algorithms suffer from low sample utilization and susceptibility to noise. Moreover, the presence of indoor obstacles significantly affects positioning accuracy and leads to the issue of wall-penetrating positioning. To address these problems, this paper proposes a hybrid time-of-arrival/angle-of-arrival (TOA/AOA) indoor positioning algorithm based on sparse reconstruction and particle filtering-based map matching. Specifically, sparse reconstruction is employed to improve the utilization of samples, and iterative updating of the position estimation is performed during the multi-sample joint estimation process to enhance accuracy. Furthermore, to tackle the problem of wall-penetrating positioning, a particle filtering-based map matching algorithm is proposed to detect and eliminate the wall-penetrating particles using the map information matrix, which optimizes the positioning results obtained from sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed algorithm in satisfying the demand for high-precision indoor positioning.

The millimeter-wave (mmwave) massive multiple input multiple output (MIMO) system in beamspace can greatly reduce the number of radio frequency (RF) links through beam selection. However, the limited number of RF links make it very difficult to obtain high-dimensional beam space channel state information. Aiming at the problems of high complexity and low accuracy of beamspace channel estimation in mmwave massive MIMO system, a block support detection (BSD) algorithm based on block sparsity is proposed. Specifically, an equivalent vector with block sparsity is obtained by recombining the path components of the channel vector, then the non-zero elements of each path component are treated together to obtain the block sparsity support set. Thus, the high-dimensional beamspace channel can be estimated under the condition of low pilot overhead. Theoretical analysis and simulation results show that the proposed BSD algorithm is superior to some existing schemes in terms of estimation accuracy, pilot overhead and complexity.

The codebook-based techniques are extensively utilized in analog beamforming and combining to overcome high path-loss in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communications. However, to find the best analog precoder and combiner, a complex search based on predefined codebook is required in conventional schemes, which leads to large time cost. For the purpose of reducing complexity, we propose a new integer coded quantified angles-based particle swarm optimization (IC-PSO) beamforming algorithm. We firstly propose a joint search scheme based on integer coded (IC) quantified angles which transforms the search space into the integer field to simplify the search space. To converge to the optimal solution quickly, an improved particle swarm optimization (PSO) algorithm is further proposed. In this way, the best precoder and combiner can be found with lower complexity. Furthermore, we optimize the inertia weight and acceleration coefficients and process the out-of-bounds particles, which can improve the search ability of the PSO. Theoretical analysis indicates that the proposed IC-PSO beamforming has the lower complexity than some existing methods. Simulation results show that the algorithm has a satisfactory achievable rate which can achieve almost 98% performance of the full-search beamforming.