Unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communication is vulnerable to malicious jamming due to the broadcast nature of wireless communications. In this letter, an anti-jamming power control framework with an unknown jamming model and unknown transmission power is proposed. In particular, the probability density function (PDF) of the effective jamming signal strength (EJSS) is first estimated via kernel density estimation (KDE). Then, utilizing the EJSS, a deep deterministic policy gradient (DDPG) based framework is proposed to acquire the power control strategy in real time. Moreover, a trajectory design scheme based on K-means++ is proposed to track the location of users. The simulation results show that the proposed framework yields an improved sum rate and energy efficiency over the reference schemes.

In this letter, we investigate user-centric access point (AP) selection for cell-free massive multiple-input multiple-output (MIMO) systems. In contrast to existing works, we study this problem from the perspective of game theory and model user-centric AP service cluster formation as a local altruistic game, which is proven to be an exact potential game (EPG) and to have at least one pure Nash equilibrium (NE). Then, an improved maximum non-neighbor-set-based concurrent spatial adaptive play (MNSB-C-SAP) algorithm is proposed to achieve the NE. Simulation results show that the proposed algorithm outperforms existing algorithms in terms of convergence speed and verify the superiority of the game-theoretic AP selection method compared with some existing approaches.

In this paper, a direction of arrival (DoA) estimation and tracking scheme based on spatial structure matching is proposed for integrated sensing and communication (ISAC) massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Firstly, the frame structure that enables DoA estimation and tracking as well as data transmission is introduced. Then, a pilot signal preprocessing method for robust DoA estimation and tracking is proposed, including suppression of beam squint effect and multipath combination of pilot sequences. Finally, a DoA estimator design based on spatial structure matching for ISAC massive MIMO-OFDM system is proposed, including an initial DoA estimation scheme based on preamble and a DoA tracking scheme based on embedded pilot. The simulation results show that the proposed spatial structure matching based DoA estimation and tracking method can adapt to the dynamic changes of scattering environment and obtain accurate DoA estimation under low signal-to-noise ratio, and the estimation performance is superior to the traditional multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance technique (ESPRIT) methods and their spatial smoothing enhancement schemes. Moreover, the computational complexity of the proposed scheme is significantly lower than that of the MUSIC and ESPRIT methods.

Underdetermined blind DOA estimation for linear arrays without the prior knowledge of array configuration is a challenging problem. Two methods are proposed to solve this problem based on a moving platform. The first one exploits the structure of the equivalent array manifold matrix constructed by using several different time intervals for the array motions and utilizes iterative optimization to obtain the estimation of DOAs. By considering secondary time intervals for the array motions further, the other combines the DOA matrix and multistage Wiener filter (MSWF) to estimate DOAs in a more costeffective way. Simulation results and complexity analysis testify the effectiveness and superiority of the proposed algorithms.

This paper proposes a new source number estimator with improved degrees of freedom for blind source separation, where the mixing matrix does not have the parameterized structure. In order to enhance the degrees of freedom, we exploit the sample dependence of each source and construct a new matrix by vectorizing some delayed covariance matrices based on Khatri-Rao product. Then, an improved Gerschgorin disk estimator for source number is presented based on the new matrix. This estimator can detect the number of sources up to 2M - 1 using only M sensors, while the traditional source number estimators can merely estimate the number of sources less than M employing the same number of sensors. Simulation results verify the superiority of the proposed method by comparing with the existing source enumeration methods in scenario of spatially non-uniform noise when the sensor number is more than the source number and validate the reliability of the proposed method in the case with fewer sensors than sources.