Minglu Jin

Reconfigurable intelligent surfaces (RIS) can accurately manipulate and enhance the propagation paths and attributes of millimeter-wave (mmWave) signals, which plays a crucial role in maximizing the capacity and speed of communication networks. However, the presence of multiple reflective elements in RIS leads to propagation delays and results in the formation of high-dimensional channel matrices in RIS-assisted mmWave multiple-input single-output (MISO) communication systems. This paper aims to investigate the joint channel estimation and localization to face the challenges related to the pilot overhead for channel estimation, and the synchronization and complexity of localization. To reduce pilot overhead and ensure accurate channel estimation, a distributed Adam optimization and deep neural network (Adam-DNN)-based channel estimation method is proposed, in which a distributed network utilizing Adam optimization and the incorporation of batch normalization to target internal covariate shift (ICS). Subsequently, a lowcomplexity localization technique utilizing inverse fast Fourier transform (IFFT) and quasi-Newton algorithm is employed to attain accurate estimations of propagation delay and angle of departure (AOD). Furthermore, through the integration of the estimated channel parameters, the determination of clock bias is facilitated, addressing the synchronization challenge in localization, and ultimately enabling accurate user positioning. Simulation results illustrate that the suggested methods display a high level of accuracy and robustness.

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.

This paper considers the problem of radar target detection in compound Gaussian clutter background. Different from the existing detector design criteria, we propose two new detection schemes for the detection problem from the optimization perspective. Specifically, in the first scheme, the detection problem is firstly studied by introducing an auxiliary variable and transforming it into a maximum likelihood estimation problem. Under this scheme, the maximum likelihood detector and its improved version with parameter estimation are developed by using maximum likelihood criterion. In the second scheme, the detection problem is recast into a binary quadratic programming (BQP) problem. Resorting to the global optimality conditions and solution for the BQP problem, we design four BQP detectors named BQPH, BQPS, BQPM and BQPW with the aid of hard decision fusion, data fusion based on summation and taking median, and whitening respectively. At the analysis stage, the statistical distributions of the BQP detectors are modelled using $t$ location-scale distribution, and the theoretical closed-form expressions of the false alarm probability, thresholds and detection probability of the four BQP detectors are further derived. Finally, simulation experiments on the simulated data and real sea clutter data are performed to highlight the effectiveness of the proposed detectors in comparison with several state-of-the-art detectors.

Proactive eavesdropping was used recently to efficiently intercept a suspicious wireless communication link, by jamming the suspicious destination node. However, While jamming helps weaken the suspicious link, it does not improve the eavesdropping channel from the source node to the legitimate eavesdropper. This work proposes to use intelligent reflecting surfaces (IRS) to enhance proactive eavesdropping, by jointly affecting both the suspicious and eavesdropping channels, and is the first to employ the cooperative passive beamforming in the double-IRS aided monitoring system. By considering the cooperative two single-reflection links and especially the double-reflection link, we jointly design the passive phase shift matrices of double IRSs to optimize the eavesdropping capability. Due to the coupled variables caused by double-IRS cooperation and the intractable unit modulus constraints of IRS elements, the non-convex optimization problem is difficult to solve. We first divide the problem into two subproblems, and apply the idea of minimization majorization to make each subproblem convex. We obtain the solution of reflective coefficient matrix in closed form, and then propose an alternating algorithm with low complexity to obtain the Karush-Kuhn-Tucker (KKT) solution. Finally, simulation results are presented to demonstrate the effectiveness of cooperative passive beamforming design over the traditional single-IRS and jamming assisted eavesdropping schemes.

This letter considers an optimal signal detection problem for uplink user-centric cloud radio access network (C-RAN). In user-centric C-RAN, users communicate with nearby remote radio heads (RRHs), and RRHs are connected to baseband unit (BBU) pool. Then, the baseband processing functionalities are migrated to the BBU pool to provide joint signal detection for a number of users. However, joint signal processing will encounter high computational complexity as the network scale grows. Especially for optimal signal detection, the computational complexity increases exponentially as the number of users increases. To address this issue, we propose a symbol-wise maximum likelihood (ML) detection algorithm to achieve optimal performance with low complexity. The key to realize the symbol-by-symbol separate detection is that it makes full use of the sparsity of the channel matrix in user-centric C-RAN. We further provide the theoretical derivation to clarify the superiority of the proposed algorithm in this sparse channel scenario. Numerical experiments show that the proposed algorithm can achieve optimal performance with low complexity in user-centric C-RAN.

In this paper, two widely used spectrum sensing algorithms, sphericity test and energy detection are considered. The statistics of sphericity test and energy detector with estimated noise power are weighted and fused to propose a mixture detector. The proposed mixture detector based on the fusion test statistics can simultaneously utilize the power information and correlation feature of the received signals. Specifically, the theoretical optimal weight is derived by maximizing the deflection coefficient of the mixture detector. Besides, the analytical expressions for the decision threshold, false alarm probability and detection probability of this algorithm are derived by moment-matching Beta approximation. Numerical examples are provided to illustrate the validity of theoretical analysis. And simulations show that the proposed mixture detector with optimal weight can improve the spectrum sensing performance compared with existing algorithms.

Due to the proliferation of mobile devices, provisioning of massive connectivity has become a major challenge for future networks. The combination of millimeter wave (mmWave) with non-orthogonal multiple access (NOMA) provides a promising solution to massive connectivity. However, the security issue therein cannot be ignored due to the openness of wireless channels. To overcome the security challenge in mmWave-NOMA based networks, the nonorthogonal interference can be exploited to improve the security. In this paper, we propose a novel mmWave-NOMA framework where the users are classified as secure users (SUs) and common users (CUs), to satisfy their heterogeneous security service needs with the presence of randomly located eavesdroppers. According to their channel disparity, the NOMA users with stronger channel gains are deemed as SUs for better secrecy performance, while the remaining ones are served as CUs. To further enhance the security, hybrid precoding for SUs is designed to strengthen the desired signal and reduce interference. In addition, to reduce the complexity and satisfy the diverse demands, user grouping and power allocation are jointly optimized to maximize the sum rate of CUs subject to the SUs’ requirements. To solve the intractable non-convex problem, we decompose it into two subproblems, i.e., user grouping and power optimization, and a hybrid SU-CU grouping algorithm and a successive convex approximation based algorithm are proposed to solve them, respectively. Finally, simulation results are provided to show the advantages of the proposed scheme.

In this letter, the correlation structure inherent in the received data is exploited to improve detection performance for sea-surface small floating targets in short observation time. Three detectors are devised resorting to the eigenvalues of covariance matrix that are versatile statistics reflecting the signal correlation. Specifically, the proposed detectors respectively exploit the maximum eigenvalue to arithmetic mean (MAM) of all eigenvalues, the maximum eigenvalue to geometric mean (MGM) of all eigenvalues, and the maximum eigenvalue to minimum eigenvalue (MME) to form test statistics. At the analysis stage, the three-parameter Burr function is exploited to approximate the statistical distributions of the test statistics under null hypothesis and alternative hypothesis. Besides, the analytic expressions of false alarm probability, detection probability, and thresholds of the proposed detectors are derived. Finally, simulation results on real sea clutter show that the proposed detectors provide effective solutions to the problem of target detection in sea clutter.