Weidong Mei

Qi Dai, Beixiong Zheng, Yanhua Tan et al.

Rotatable antenna (RA) technology has emerged as a promising solution to enhance spectrum efficiency by exploiting additional spatial degrees of freedom (DoFs) in multiple access networks. However, the relative performance superiority among different multiple access schemes remains largely unclear due to the unique capability of RA in reconfiguring the directional gain pattern. In this letter, we conduct a theoretical comparison between non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes in RA-assisted communication systems in terms of transmit power minimization, subject to constraints on antenna rotational range and users' target rates. To address the associated non-convex optimization problem, a particle swarm optimization (PSO) algorithm is employed to optimize the rotational angle. Simulation results demonstrate that RA-assisted schemes significantly reduce transmit power compared to fixed-antenna benchmarks. Furthermore, RA-assisted NOMA may perform worse than time-division multiple access (TDMA) for symmetric user deployments, while it exhibits superior robustness and energy efficiency in asymmetric scenarios.

Qi Dai, Beixiong Zheng, Yanhua Tan et al.

Unlike conventional fixed-antenna architectures, rotatable antenna (RA) has shown great potential in enhancing wireless communication performance by exploiting additional spatial degrees of freedom (DoFs) in a cost-effective manner. In this letter, we propose a novel RA-enabled covert communication system, where an RA array-based transmitter (Alice) sends covert information to a legitimate user (Bob) in the presence of multiple wardens (Willies). To maximize the covert rate, we optimize the transmit beamforming vector and the rotational angles of individual RAs, subject to the constraints on covertness, transmit power, and antenna rotational range. To address the non-convex formulated problem, we decompose it into two subproblems and propose an efficient alternating optimization (AO) algorithm to solve the two subproblems iteratively, where the second-order cone programming (SOCP) method and successive convex approximation (SCA) approach are applied separately. Simulation results demonstrate that the proposed RA-enabled covert communication system can provide significantly superior covertness performance to other benchmark schemes.

Qiyao Wang, Beixiong Zheng, Xue Xiong et al.

In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees of freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna boresight directions can be independently adjusted to proactively improve wireless channel conditions for latency-critical users. We aim to minimize the maximum computation latency by jointly optimizing the MEC server computing resource allocation, receive beamforming, and the deflection angles of all RAs. To address the resulting non-convex problem, we develop an efficient alternating optimization (AO) framework. Specifically, the optimal edge computing resource allocation is derived based on the Karush-Kuhn-Tucker (KKT) conditions. Given the computing resources, the receive beamforming is optimized using semidefinite relaxation (SDR) combined with a bisection search. Furthermore, the RA deflection angles are optimized via fractional programming (FP) and successive convex approximation (SCA). Simulation results verify that the proposed RA-enabled MEC scheme significantly reduces the maximum computation latency compared with conventional benchmark methods.

Xin Wei, Weidong Mei, Xuan Huang et al.

Movable antennas (MAs) offer additional spatial degrees of freedom (DoFs) to enhance communication performance through local antenna movement. However, to achieve accurate and fast antenna movement, MA drivers entail non-negligible mechanical power consumption, rendering energy efficiency (EE) optimization more critical compared to conventional fixed-position antenna (FPA) systems. To address this issue, we develop a fundamental power consumption model for stepper motor-driven multi-MA systems based on electric motor theory. Based on this model, we formulate an EE maximization problem from a multi-MA base station (BS) to multiple single-FPA users. We aim to jointly optimize the MAs' positions, moving speeds, and the BS's transmit precoding matrix subject to collision-avoidance constraints during the multi-MA movements. However, this problem is difficult to solve. To tackle this challenge, we first reveal that the collision-avoidance constraints can always be relaxed without loss of optimality by properly renumbering the MA indices. For the resulting relaxed problem, we first consider a simplified single-user setup and uncover a hidden monotonicity of the EE performance with respect to the MAs' moving speeds. To solve the remaining optimization problem, we develop a two-layer optimization framework. In the inner layer, the Dinkelbach algorithm is employed to derive the optimal beamforming solution for any given MA positions. In the outer layer, a sequential update algorithm is proposed to iteratively refine the MA positions based on the optimal values obtained from the inner layer. Next, we proceed to the general multi-user case and propose an alternating optimization (AO) algorithm. Numerical results demonstrate that despite the additional mechanical power consumption, the proposed algorithms can outperform both conventional FPA systems and other existing EE maximization benchmarks