Movable antenna (MA) technology can flexibly reconfigure wireless channels by adjusting antenna positions in a local region, thus owing great potential for enhancing communication performance. This letter investigates MA technology-enabled multiuser uplink communications over general Rician fading channels, which consist of a base station (BS) equipped with the MA array and multiple single-antenna users. Since it is practically challenging to collect all instantaneous channel state information (CSI) by traversing all possible antenna positions at the BS, we instead propose a two-timescale scheme for maximizing the ergodic sum rate. Specifically, antenna positions at the BS are first optimized using only the statistical CSI. Subsequently, the receiving beamforming at the BS (for which we consider the three typical zero-forcing (ZF), minimum mean-square error (MMSE) and MMSE with successive interference cancellation (MMSE-SIC) receivers) is designed based on the instantaneous CSI with optimized antenna positions, thus significantly reducing practical implementation complexities. The formulated problems are highly non-convex and we develop projected gradient ascent (PGA) algorithms to effectively handle them. Simulation results illustrate that compared to conventional fixed-position antenna (FPA) array, the MA array can achieve significant performance gains by reaping an additional spatial degree of freedom.

In this letter, we investigate an unmanned aerial vehicle (UAV) communication system, where an intelligent reflecting surface (IRS) is deployed to assist in the transmission from a ground node (GN) to the UAV in the presence of a jammer. We aim to maximize the average rate of the UAV commnunication by jointly optimizing the GN’s transmit power, the IRS’s passive beamforming and the UAV’s trajectory. However, the formulated problem is difficult to solve due to the non-convex objective function and the coupled optimization variables. Thus, to tackle it, we propose an alternating optimization (AO) based algorithm by exploiting the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques. Simulation results show that the proposed algorithm can significantly improve the average rate compared with the benchmark algorithms. Moreover, it also shows that when the jamming power is large and the number of IRS elements is relatively small, deploying the IRS near the jammer outperforms deploying it near the GN, and vice versa.

Intelligent reflective surfaces (IRSs) as low energy consumption and easy to attach devices have been widely applied in the field of anti-jamming recently. In particular, the combination of IRS and unmanned aerial vehicle (UAV), as IRS-UAV, further expands the scope of IRS services. In this article, the joint IRS selection and beamforming optimization problem has been investigated in multiple IRS-UAV-assisted anti-jamming D2D networks. To solve the above optimization problem, a distributed matching-based selection and $Q$ -learning-based beamforming optimization algorithm (DMQ) was proposed. In detail, the optimization problem is decomposed into two subproblems, namely, the IRS selection subproblem is formulated as a noncommutative many-to-many matching game model to describe peer effects and uncertainty selection quotas, and the passive beamforming optimization subproblem is solved by a reinforcement algorithm to satisfy the complex environment. Numerical simulations confirm the convergence and near-optimal performance of the proposed scheme with lower latency and greater robustness.

The primary objective of anti-jamming techniques is to ensure that the transmitted data arrives at the intended receiver without being disturbed or jammed with by any jamming signal or other hostile activities to ensuring the security of the communication system. Deep reinforcement learning (DRL) has been extensively utilized in solving the dynamic spectrum anti-jamming problem. However, most of existing DRL-based algorithms require lots of training time, which fails to adapt the fast-channging jamming environment. Our objective is to find a practical and fast-convergence anti-jamming learning solution. To achieve this, we redesign the DRL algorithm in the following two ways. First, we split the cycle of reinforcement learning into two parts: applying process and training process. Second, we use soft labels instead of rewards which bring more information. We further theoretically show that the information gain can help our proposed algorithm converge faster. Moreover, we also show that our labeled DRL algorithm is better than the idealized DRL-based scheme which can obtain the same information as the soft labels. Simulation results demonstrate that compared with existing DRL-based algorithms, our proposed algorithm reduces the number of iterations by up to 90%.

In conventional cooperative non-orthogonal multiple access (NOMA) networks, spectral efficiency loss occurs due to a half-duplex constraint. To address this issue, we propose an incremental cooperative NOMA (ICN) protocol for a two-user downlink network. In particular, this protocol allows the source to adaptively switch between a direct NOMA transmission mode and a cooperative NOMA transmission mode according to a 1-bit feedback from the far user. We analytically prove that the proposed ICN protocol outperforms the conventional cooperative NOMA protocol. In addition, an optimal power allocation strategy at the source is studied to minimize the asymptotic system outage probability. Finally, numerical results validate our theoretical analysis, present insights, and quantify the enhancement achieved over the benchmark scheme.

This paper considers a multi-jammer-assisted covert communication scenario, where a transmitter communicates with a receiver without being detected by a warden under the assistance of multiple friendly jammers. The transmission rate maximization problem subjecting to the covertness constraint by jointly optimizing the jammer selection strategy and power control of the transmitter is studied under the following three channel models: 1) Additive white Gaussian noise (AWGN) channel; 2) Block fading channel without channel state information (CSI) acquisition cost; 3) Block fading channel with CSI acquisition cost. Under the AWGN channel, considering achievable schemes of the transmitter, relative entropy is adopted to lower bound the detection error probability at the warden. We decouple the joint optimization problem into two sub-problems and prove that selecting one jammer with the maximum ratio of the jamming power at the warden to that at the receiver is optimal with proper transmit power control in an interference-limited environment. Motivated by this key analytical conclusion, we then study the second channel model's optimal single jammer selection strategy. Considering the worst case that the warden employs the optimal detector, we analytically show that the optimal selection criterion in this channel model is choosing the jammer with the maximum ratio of the average jamming power at the warden to the instantaneous jamming power at the receiver. Given this selection criterion, we then study the problem of how many jammers should be explored when the acquisition cost of jammers' CSI cannot be ignored. We formulate the optimization problem as an optimal stopping problem and propose two optimal strategies for two cases that whether the previously explored jammers are allowed to select. The numerical results evaluate the theoretical results and show the superiority of the proposed jammer selection strategies. Furthermore, we also present the impact of some key parameters on the covert rate, such as the covertness constraint, the number of jammers, and the CSI acquisition time of one jammer, which provide more valuable insights.