As a core technology for next-generation wireless communication, intelligent reflecting surfaces (IRSs) provide a new solution to the problems of ever-increasing cost and energy consumption in wireless communication. This letter studies a new application scenario for IRSs—using IRSs for user positioning. Multiple aerial intelligent reflecting surfaces (AIRSs) are deployed in the air to locate users in a certain area. First, we propose an angle-domain approach to obtain the angle of arrival (AoA) information of the direct path between each user and AIRS. Then, we use the AoA positioning strategy at the access point (AP) to locate users. Experimental results show that the proposed positioning strategy has good positioning performance.

Cell-free massive multiple-input multiple-output (mMIMO) is regarded as a promising technology in the future wireless communication. This paper aims to minimize the downlink power consumption of cell-free mMIMO system under the downlink rate constraints of users and the power constraints of per-antenna over spatially correlated Rayleigh fading channels. Firstly, this paper studies the performance of two different downlink transmission modes: non-coherent joint transmission and coherent joint transmission. Then, for the two transmission modes, the corresponding downlink power optimization model is established and an efficient power optimization algorithm is proposed based on the Lagrange multiplier (LM) method. Under the power optimization model, this paper analyzes the impact of different factors on the downlink power consumption of cell-free mMIMO system. Simulation results show that coherent joint transmission performs better than non-coherent joint transmission on spectral efficiency (SE) and energy efficiency (EE). In addition, under the same power control strategy, the downlink power consumption of cell-free mMIMO is much lower than co-located mMIMO. Moreover, the total transmission power decreases when more antennas are utilized at each access point (AP) or spatial correlation of channels becomes weak.

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.

The intelligent reflecting surface (IRS) is considered a core technology of next-generation mobile communication. It has significant advantages in enhancing network coverage, spectrum efficiency, energy efficiency, and deployment cost. Compared with the conventional massive multiple-input-multiple-output (MIMO) system, cell-free massive MIMO overcomes the limitation imposed by inter-cell interference in traditional cellular mobile networks and realizes coherent transmission centered on users. In this article, we consider a new application scenario for the IRS — an aerial IRS (AIRS)-aided cell-free massive MIMO system where multiple APs serve several users through an AIRS. The users are in a “shadow area” where we cannot provide good quality of service (QoS) due to the remote location and the shelter of tall buildings. Our goal is to optimize the power allocation and beamforming of each AP, the placement and reflection phase shift parameters of the AIRS to maximize the user’s achievable rate. Firstly, we consider the optimization for fixed placement of the AIRS, where we propose a joint optimization strategy to maximize the achievable rate of the user. Then, we propose a fast optimal location search algorithm base on the path loss model to determine the optimal location of the AIRS and decrease the computational complexity. The simulation results show that the proposed methods can improve the performance for the achievable rate of the system. To the best of our knowledge, this article is the first to study the application scenario for the combination of IRS technology and a cell-free massive MIMO system.

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, we propose a channel feature projection clustering (CFPC) based joint channel state information and direction of arrival (JCDoA) estimation scheme for integrated sensing and communication (ISAC) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Specifically, the spatial-delay domain coarse channel estimation scheme is introduced first, and then the angular-domain coarse direction of arrival (DoA) estimation is presented. In order to improve the performance of channel estimation, the angular subspace pruning (ASP) based channel state information (CSI) refinement scheme is proposed. Finally, the DoA refinement scheme based on angular-domain CFPC is proposed. The simulation results show that the proposed JCDoA scheme outperforms traditional least square (LS) and linear minimum mean square error (LMMSE) channel estimation schemes, multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance technique (ESPRIT) DoA estimation methods and their spatial smoothing (SS) enhancement schemes.