In this paper, we propose a downlink intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) for millimeter-wave (mmWave) massive MIMO with lens antenna array, i.e., IRS-aided mmWave beamspace NOMA, where the single-antenna users without direct-link but connected to the base station (BS) with the aid of the IRS are grouped as one NOMA group. Considering the power leakage problem in beamspace channel and the per-antenna power constraint, we propose two multi-beam selection strategies for the BS-IRS link under two channel models, i.e., 2-dimension (2D) channel model and 3-dimension (3D) channel model, respectively, where two corresponding RF chain configuration strategies are designed, respectively. Then, we formulate and solve the optimization problem for maximizing the weighted sum rate by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, where we propose the alternating optimization (AO) method to solve the above joint optimization problem. Especially, different from the stochastic method, based on the beam-splitting technique, we propose the method to initialize the feasible solution for the proposed AO method, where the conditional transmit power minimization problem is formulated and solved. Through simulations, the weighted sum rate performance of the proposed IRS-aided mmWave beamspace NOMA is verified.

Active reconfigurable intelligent surface (RIS) draws great attentions recently due to its unique nature of “changing” the wireless propagation channels via simultaneously adjusting the phase and amplifying the amplitude of signals, and is proved to effectively overcome the double fading effect when the signals are reflected by RIS compared with the widely investigated passive RIS with only adjusting the phase of the signals. Motivated by this great advantage, in this paper, we apply active RIS into a multi-user system and focus on enhancing the secure transmission performance in the presence of a malicious eavesdropper (Eve). Specifically, two types of imperfect channel state information (CSI) between RIS and Eve, namely imperfect CSI with bounded estimation error and with statistical distribution of estimation error, are considered, and we focus on enhancing the worst-case secrecy rate (SR) as well as weighted sum-SR of this system under each CSI condition. In particular, for the study of weighted sum-SR maximization, a more serious situation is assumed where Eve can filter out all the interference signals as well as the external noise generated by RIS when decoding each user's information. Given these settings, we propose a robust alternating optimization (AO)+line search combined algorithm to optimize the worst-case SR, and a dual-successive approximation algorithm to optimize the weighted sum-SR. Numerical examples demonstrate that compared with the benchmark scheme under passive RIS aided case, the proposed algorithms for the active RIS aided system greatly enhance both the worst-case and weighted-sum SR under these CSI conditions.

Recently, the Transformer-based technique has emerged as a promising solution for modeling contextual information in Remote Sensing (RS) scenes and has found widespread applications in RS scene classification. However, how to make full use of intermediate features learned in Transformers is of crucial importance in the RS scene classification tasks. Therefore, this paper proposes a Hierarchical Feature Fusion of Transformer with Patch Dilating (HFFT-PD), which aims to capture rich contextual information from hierarchical features to enhance the performance of RS scene classification. Specifically, the HFFT-PD model consists of a Hierarchical Transformer Merging (HTM) block and a Lightweight Adaptive Channel Compression (LACC) module, in which the HTM is specially designed for the Transformer architecture to bridge the semantic gaps between features from different hierarchical blocks, and the LACC accounts for the significance of distinct channels in the ultimate classification features. In addition, a brand-new Patch Dilating strategy is uniquely designed for the Transformer paradigm, functioning as a reassembly operator predicated on patch features. Contrasting with conventional upsampling techniques, Patch Dilating facilitates upsampling without requiring supplementary information, while concurrently preserving the semantic content of local spatial structure. Extensive and rigorous experiments conducted on the UCM, AID, and NWPU-45 datasets, with training ratios of 80%, 50%, and 20% respectively, demonstrate that our proposed HFFT-PD outperforms the baseline at least by 0.59%, 0.44%, and 0.99% respectively, showcasing the significant superiority of our HFFT-PD over contemporary state-of-the-art methodologies.

Different from the conventional single-beam beamspace non-orthogonal multiple access (NOMA) scheme, where the users only within the same analog beam can be served by the NOMA technique, in this paper, we propose a multi-beam NOMA scheme for the millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with lens antenna array communication system. The proposed scheme can break the single-beam constraint and perform NOMA transmission within the different analog beams. Then, we design the power allocation for minimizing the total power consumption, which mainly includes two stages. In the first stage, after performing beam scanning and beam selection, we design the game-theory based user grouping method for minimizing the conditional total power consumption. In the second stage, with the achieved user grouping solution and the corresponding beamspace channel state information (CSI), we formulate and solve the power allocation problem for minimizing the total power consumption. In addition, given the user grouping solution for minimizing the total power consumption, for evaluating the sum rate performance of the proposed multi-beam beamspace MIMO-NOMA scheme, we formulate and solve the power allocation optimization problem for maximizing the sum rate. In simulations, compared with the single-beam beamspace MIMO-NOMA scheme, given the user grouping solution, the proposed scheme has inferior sum rate performance when the power budget is enough high. However, at the cost of a little higher transmit power consumption, the proposed scheme has lower total power consumption and can simultaneously serve more users with the same number of RF chains.

The active intelligent reflecting surface (IRS) can amplify the incident signal besides reflecting, thus alleviating the double-fading effect of the cascaded link. However, if the interference caused by the cascaded link cannot be effectively cancelled, the alleviated effect may result in increased interference among users, thus limiting the performance gain brought by the active IRS. To this end, an active IRS aided rate-splitting multiple access (RSMA) scheme is proposed in this paper for the millimeter-wave hybrid antenna array. In the proposed scheme, a user grouping method is designed to circumvent the narrow beamwidth constraint, and the 2-layer rate-splitting (RS) strategy applicable to the user grouping result and the corresponding successive interference cancellation are designed to cancel interference. To evaluate the max-min fairness (MMF) performance of the proposed scheme, an MMF optimization problem (OP) is formulated, where the digital precoder, the RS vector, and the reflecting vector are jointly optimized. An alternating optimization (AO) algorithm with a novel AO strategy is proposed to solve the MMF OP, where the RS vector is optimized in two subproblems. An initialization method is designed specially to start the AO algorithm. Simulation results illustrate that the proposed scheme can more fully exploit the active IRS, and although the active IRS consumes additional power, it can bring energy efficiency gain to system.

The security of user-centric cell-free massive multiple-input multiple-output (UC-CF mMIMO) system in the presence of multiple collusive eavesdroppers (Eves) is investigated in this letter. The access points (APs) share the estimated channel state informations (CSIs) of serving users to central processing unit (CPU). Utilizing the received partial CSIs, the CPU optimize precoding via formulating a secracy rate maximization problem under minimum rate requirements of users and power constraints of APs. The formulated problem is non-convex, and the optimum solution is acquired by iteratively solving a sequence of second-order cone programming problems. The numerical simulations present the comparison of secrecy performance achieved in different AP selection method of UC-CF system, and CF system.