This paper proposes a novel towed movable antenna (ToMA) array architecture to enhance the physical layer security of airborne communication systems. Unlike conventional onboard arrays with fixed-position antennas (FPAs), the ToMA array employs multiple subarrays mounted on flexible cables and towed by distributed drones, enabling agile deployment in three-dimensional (3D) space surrounding the central aircraft. This design significantly enlarges the effective array aperture and allows dynamic geometry reconfiguration, offering superior spatial resolution and beamforming flexibility. We consider a secure transmission scenario where an airborne transmitter communicates with multiple legitimate users in the presence of potential eavesdroppers. To ensure security, zero-forcing beamforming is employed to nullify signal leakage toward eavesdroppers. Based on the statistical distributions of locations of users and eavesdroppers, the antenna position vector (APV) of the ToMA array is optimized to maximize the users’ ergodic achievable rate. Analytical results for the case of a single user and a single eavesdropper reveal the optimal APV structure that minimizes their channel correlation. For the general multiuser scenario, we develop a low-complexity alternating optimization algorithm by leveraging Riemannian manifold optimization. Simulation results confirm that the proposed ToMA array achieves significant performance gains over conventional onboard FPA arrays, especially in scenarios where eavesdroppers are closely located to users under line-of-sight (LoS)-dominant channels.

Movable antenna (MA) is an emerging technology which can reconfigure wireless channels via adaptive antenna position adjustments at transceivers, thereby bringing additional spatial degrees of freedom for improving system performance. In this paper, from a security perspective, we exploit the MA-enabled legitimate jammer (MAJ) to subvert suspicious multiuser downlink communications consisting of one suspicious transmitter (ST) and multiple suspicious receivers (SRs). Specifically, our objective is to minimize the benefit (the sum rate of all SRs or the minimum rate among all SRs) of such suspicious communications, by jointly optimizing antenna positions and the jamming beamforming at the MAJ. However, the key challenge lies in that given the MAJ’s actions, the ST can reactively adjust its power allocations to instead maximize its benefit for mitigating the unfavorable interference. Such flexible behavior of the ST confuses the optimization design of the MAJ to a certain extent. Facing this difficulty, corresponding to the above two different benefits: i) we respectively determine the optimal behavior of the ST given the MAJ’s actions; ii) armed with these, we arrive at two simplified problems and then develop effective alternating optimization based algorithms to iteratively solve them. In addition to these, we also focus on the special case of two SRs, and reveal insightful conclusions about the deployment rule of antenna positions at the MAJ. Furthermore, we analyze the ideal antenna deployment scheme at the MAJ for achieving the globally performance lower bound. Numerical results demonstrate the effectiveness of our proposed schemes compared to conventional fixed-position antenna (FPA) and other competitive benchmarks.

The movable antenna (MA) technology has attracted great attention recently due to its promising capability in improving wireless channel conditions by flexibly adjusting antenna positions. To reap maximal performance gains of MA systems, existing works mainly focus on MA position optimization to cater to the instantaneous channel state information (CSI). However, the resulting real-time antenna movement may face challenges in practical implementation due to the additional time overhead and energy consumption required, especially in fast time-varying channel scenarios. To address this issue, we propose in this paper a new approach to optimize the MA positions based on the users’ statistical CSI over a large timescale. In particular, we propose a general field response based statistical channel model to characterize the random channel variations caused by the local movement of users. Based on this model, a two-timescale optimization problem is formulated to maximize the ergodic sum rate of multiple users, where the precoding matrix and the positions of MAs at the base station (BS) are optimized based on the instantaneous and statistical CSI, respectively. To solve this non-convex optimization problem, a log-barrier penalized gradient ascent algorithm is developed to optimize the MA positions, where two methods are proposed to approximate the ergodic sum rate and its gradients with different complexities. Finally, we present simulation results to evaluate the performance of the proposed design and algorithms based on practical channels generated by ray-tracing. The results verify the performance advantages of MA systems compared to their fixed-position antenna (FPA) counterparts in terms of long-term rate improvement, especially for scenarios with more diverse channel power distributions in the angular domain.

This paper investigates energy efficiency maximization for movable antenna (MA)-aided multi-user uplink communication systems by considering the time delay and energy consumption incurred by practical antenna movement. We first examine the special case with a single user and propose an optimization algorithm based on the one-dimensional (1D) exhaustive search to maximize the user’s energy efficiency. Moreover, we derive an upper bound on the energy efficiency and analyze the conditions required to achieve this performance bound under different numbers of channel paths. Then, for the general multi-user scenario, we propose an iterative algorithm to fairly maximize the minimum energy efficiency among all users. Simulation results demonstrate the effectiveness of the proposed scheme in improving energy efficiency compared to existing MA schemes that do not account for movement-related costs, as well as the conventional fixed-position antenna (FPA) scheme. In addition, the results show the robustness of the proposed scheme to imperfect channel state information (CSI) and provide valuable insights for practical system deployment.

Movable antenna (MA) has been recognized as a promising technology to improve communication performance in future wireless networks such as 6G. To unleash its potential, this paper proposes a novel architecture, namely extremely large-scale MA (XL-MA), which allows flexible antenna/subarray positioning over an extremely large spatial region for effectively enhancing near-field effects and spatial multiplexing performance. In particular, this paper studies an uplink XL-MA-enabled multiuser system, where single-antenna users distributed in a coverage area are served by a base station (BS) equipped with multiple movable subarrays. We begin by presenting a spatially non-stationary channel model to capture the near-field effects, including position-dependent large-scale channel gains and line-of-sight visibility. To evaluate system performance, we further derive a closed-form approximation of the expected weighted sum rate under maximum ratio combining (MRC), revealing that optimizing XL-MA placement enhances user channel power gain to increase desired signal power and reduces channel correlation to decreases multiuser interference. Building upon this, we formulate an antenna placement optimization problem to maximize the expected weighted sum rate, leveraging statistical channel conditions and user distribution. To efficiently solve this challenging non-linear binary optimization problem, we propose a polynomial-time successive replacement algorithm. Simulation results demonstrate that the proposed XL-MA placement strategy achieves near-optimal performance, significantly outperforming benchmark schemes based on conventional fixed-position antennas.

In this paper, we present a new wireless sensing system utilizing a movable antenna (MA) that continuously moves and receives sensing signals to enhance sensing performance over the conventional fixed-position antenna (FPA) sensing. We show that the angle estimation performance is fundamentally determined by the MA trajectory, and derive the Cramer-Rao bound (CRB) of the mean square error (MSE) for angle-of-arrival (AoA) estimation as a function of the trajectory for both one-dimensional (1D) and two-dimensional (2D) antenna movement. For the 1D case, a globally optimal trajectory that minimizes the CRB is derived in closed form. Notably, the resulting CRB decreases cubically with sensing time in the time-constrained regime, whereas it decreases linearly with sensing time and quadratically with the movement line segment's length in the space-constrained regime. For the 2D case, we aim to achieve the minimum of maximum (min-max) CRBs of estimation MSE for the two AoAs with respect to the horizontal and vertical axes. To this end, we design an efficient alternating optimization algorithm that iteratively updates the MA's horizontal or vertical coordinates with the other being fixed, yielding a locally optimal trajectory. Numerical results show that the proposed 1D/2D MA-based sensing schemes significantly reduce both the CRB and actual AoA estimation MSE compared to conventional FPA-based sensing with uniform linear/planar arrays (ULAs/UPAs) as well as various benchmark MA trajectories. Moreover, it is revealed that the steering vectors of our designed 1D/2D MA trajectories have low correlation in the angular domain, thereby effectively increasing the angular resolution for achieving higher AoA estimation accuracy.

Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system to fully exploit its maximum spatial diversity for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP’s antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Moreover, to further reduce the overhead of antenna movement, we propose an offline solution for APV and AOM design based on statistical channel state information (CSI). In addition, we further extend the proposed scheme from uni-polarized to dual-polarized modes for all antennas. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA schemes can attain considerable performance improvement compared to the conventional FA-based schemes.

The sixth-generation (6G) mobile communication systems are expected to provide seamless connectivity for unmanned aerial vehicles (UAVs) to support them in fulfilling various tasks. However, the line-of-sight (LoS)-dominated channels of cellular-connected UAVs expose them to severe co-channel interference from nearby base stations (BSs), which significantly degrades communication reliability. To address this challenge, this paper investigates a movable antenna (MA)-enhanced cellular-connected UAV communication system, where the additional spatial degrees of freedom (DoFs) offered by MAs are exploited for the interference-aware UAV trajectory planning. Specifically, we formulate an optimization problem to minimize the UAV mission completion time by jointly optimizing the UAV beamforming matrix, antenna position vector (APV), UAV trajectory, and UAV–BS association, subject to constraints on signal-to-interference-plus-noise ratio (SINR) requirements, UAV mobility, and MA mobility. To overcome the inherent challenges of the continuous-time formulation, we discretize both the flight region and trajectory of the UAV, thereby reformulating the problem into a tractable discrete optimization problem. A selective uniform cost search (SUCS) algorithm is then developed for UAV trajectory planning, where the feasibility of candidate grid points is evaluated by jointly optimizing beamforming, APV, and UAV–BS association to maximize the expected SINR. Simulation results show that, compared with benchmark schemes, the proposed MA-enhanced design significantly improves the expected SINR of cellular-connected UAVs along the optimized trajectory, thereby reducing UAV mission completion time while ensuring reliable communication links.

This paper proposes an efficient method for modeling and reconstructing the channel gain map (CGM) based on virtual scatterers. Specifically, we develop a virtual scatterer model to characterize the channel gain distribution in three-dimensional (3D) space, by capturing the multi-path propagation environment structure and exploiting the angular-domain spatial correlation of scatterer response. In this model, the CGM is represented as a function over a set of tunable parameters for virtual scatterers, including their number, positions, and scatterer response coefficients (SRCs), which can be estimated from a limited number of channel gain measurements at a given set of locations within the region of interest. This new representation offers a flexible and scalable modeling framework for efficient and accurate CGM reconstruction. Furthermore, we propose a progressive estimation algorithm to acquire the scatterers’ parameters. In this algorithm, we gradually increase the number of virtual scatterers to balance the computational complexity and reconstruction accuracy, and derive the closed-form solutions of SRCs with any given number and positions of virtual scatterers. In addition, by exploiting the spatial correlation of scatterer response, we propose a Gaussian process regression (GPR)-based inference method to predict the SRCs that cannot be directly estimated. Finally, ray-tracing-based simulation results under realistic physical environments validate the effectiveness of the proposed method, demonstrating that it achieves higher reconstruction accuracy compared to conventional CGM estimation approaches, especially for the scenario with limited channel measurements.

This paper proposes a new architecture for the low-earth orbit (LEO) satellite ground station aided by movable antenna (MA) array. Unlike conventional fixed-position antenna (FPA), the MA array can flexibly adjust antenna positions to reconfigure array geometry, for more effectively mitigating interference and improving communication performance in ultra-dense LEO satellite networks. To reduce movement overhead, we configure antenna positions at the antenna initialization stage, which remain unchanged during the whole communication period of the ground station. To this end, an optimization problem is formulated to maximize the average achievable rate of the ground station by jointly optimizing its antenna position vector (APV) and time-varying beamforming weights, i.e., antenna weight vectors (AWVs). To solve the resulting non-convex optimization problem, we adopt the Lagrangian dual transformation and quadratic transformation to reformulate the objective function into a more tractable form. Then, we develop an efficient block coordinate descent-based iterative algorithm that alternately optimizes the APV and AWVs until convergence is reached. Simulation results demonstrate that our proposed MA scheme significantly outperforms traditional FPA by increasing the achievable rate at ground stations under various system setups, thus providing an efficient solution for interference mitigation in future ultra-dense LEO satellite communication networks.

Movable antenna (MA) is an emerging technology that can significantly improve communication performance via the continuous adjustment of the antenna positions. To unleash the potential of MAs in wideband communication systems, acquiring accurate channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers, is a crucial issue. In this paper, we study the channel estimation problem for wideband MA systems. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at selected position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem with multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Moreover, we also devise an alternating refinement approach to further improve the accuracy of the estimated FRVs, DRV, and PRT. This is achieved by minimizing the discrepancy between the received pilots and those constructed by the estimated CSI, which can be efficiently carried out by using the gradient descent algorithm. Finally, simulation results demonstrate that both the SOMP-based channel estimation method and alternating refinement method can reconstruct the complete wideband CSI with high accuracy, where the alternating refinement method performs better despite a higher complexity.

This paper presents an end-to-end deep learning framework in a movable antenna (MA)-enabled multiuser communication system. In contrast to the conventional works assuming perfect channel state information (CSI) for MA placement or adopting a decoupled CSI acquisition and MA placement design paradigm, we address the practical CSI acquisition issue through the design of pilot signals and quantized CSI feedback, and further incorporate the joint optimization of channel estimation, MA placement, and precoding design. The proposed mechanism enables the system to learn an optimized transmission strategy from imperfect channel data, overcoming the limitations of conventional methods that conduct channel estimation and antenna position optimization separately. To balance the performance and overhead, we further extend the proposed framework to optimize the antenna placement based on the statistical CSI. Simulation results demonstrate that the proposed approach consistently outperforms traditional benchmarks in terms of achievable sum-rate of users, especially under limited feedback and sparse channel environments. Notably, it achieves a performance comparable to the widely-adopted gradient-based methods with perfect CSI, while maintaining significantly lower CSI feedback overhead. These results highlight the effectiveness and adaptability of learning-based MA system design for future wireless systems.

Movable antenna (MA) has been regarded as a promising technology to enhance wireless communication performance by enabling flexible antenna movement. However, the hardware cost of conventional MA systems scales with the number of movable elements due to the need for independently controllable driving components. To reduce hardware cost, we propose in this paper a novel architecture named cross-linked MA (CL-MA) array, which enables the collective movement of multiple antennas in both horizontal and vertical directions. To evaluate the performance benefits of the CL-MA array, we consider an uplink multiuser communication scenario. Specifically, we aim to minimize the total transmit power while satisfying a given minimum rate requirement for each user by jointly optimizing the horizontal and vertical antenna position vectors (APVs), the receive combining at the base station (BS), and the transmit power of users. A globally lower bound on the total transmit power is derived, with closed-form solutions for the APVs obtained under the condition of a single channel path for each user. For the more general case of multiple channel paths, we develop a low-complexity algorithm based on discrete antenna position optimization. Additionally, to further reduce antenna movement overhead, a statistical channel-based antenna position optimization approach is proposed, allowing for unchanged APVs over a long time period. Simulation results demonstrate that the proposed CL-MA schemes significantly outperform conventional fixed-position antenna (FPA) systems and closely approach the theoretical lower bound on the total transmit power. Compared to the instantaneous channel-based CL-MA optimization, the statistical channel-based approach incurs a slight performance loss but achieves significantly lower movement overhead, making it an appealing solution for practical wireless systems.

Movable antenna (MA) has been regarded as a promising technology to enhance wireless communication performance by enabling flexible antenna movement. To reduce hardware cost, we propose in this paper a novel architecture named cross-linked MA (CL-MA) array, which enables the collective movement of multiple antennas in both horizontal and vertical directions. To evaluate the performance benefits of the CL-MA array, we consider an uplink multiuser communication scenario. Specifically, we aim to minimize the total transmit power while satisfying a given minimum rate requirement for each user by jointly optimizing the horizontal and vertical antenna position vectors (APVs), the receive combining at the base station (BS), and the transmit power of users. To solve this challenging non-convex optimization problem, we develop a low-complexity algorithm based on discrete antenna position optimization. Additionally, to further reduce antenna movement overhead, a statistical channel-based antenna position optimization approach is proposed, allowing for quasi-static APVs over a long time period. Simulation results demonstrate that the proposed CL-MA schemes significantly outperform conventional fixed-position antenna (FPA) systems and closely approach the theoretical lower bound on the total transmit power. Compared to the instantaneous channel-based CL-MA optimization, the statistical channel-based approach incurs a slight performance loss but achieves significantly lower movement overhead, making it an appealing solution for practical wireless systems.

In this paper, we propose to employ a movable-antenna (MA) array to enhance unmanned aerial vehicle (UAV) covert communications by fully exploiting the spatial degrees of freedom (DoFs) in large-scale adjustment of UAVs’ positions within broad areas and small-scale movement of MAs within local regions. Specifically, to guarantee fairness, we formulate an optimization problem to maximize the minimum achievable rate over all users via UAV trajectory, transmit beamforming, and antenna position design, subject to a covertness constraint. To solve this non-convex optimization problem, we develop a two-step method to obtain a sub-optimal solution. Specifically, we first design the UAV trajectory under the assumption of ideal beam patterns, which significantly decouples the UAV trajectory optimization and directional transmit beamforming. Then, an alternating optimization algorithm with the successive convex approximation (SCA) technique is developed to optimize the UAV transmit beamforming and MAs’ positions. Simulation results demonstrate that our proposed system design can effectively enhance spectrum-efficiency and stealth of UAV downlink transmissions, significantly outperform conventional systems with fixed-position antenna (FPA) arrays, and closely approach the performance upper bound with ideal beam patterns.

This paper proposes a channel estimation method for movable antenna (MA)-aided wideband communication systems to acquire complete channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at several position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem of multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Finally, simulation results demonstrate that the proposed SOMP-based channel estimation method can reconstruct the complete wideband CSI with a high accuracy.

Movable antenna (MA) is an innovative technology that can enhance channel condition by altering antenna position within a local area. Integrating MA can further improve the performance of multiuser communications in non-orthogonal multiple access (NOMA) systems. In this paper, we investigate MA enabled NOMA for multiuser downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA enabled user terminals (UTs). An optimization problem is formulated to maximize the minimum achievable rate among all the UTs by jointly optimizing the positions of MAs of each UT, the precoding matrix at BS, and the successive interference cancellation (SIC) decoding indicator matrix at UTs, subject to the limited movement area of the MAs, the maximum transmit power of the BS, and the SIC decoding condition. To solve this non-convex problem, we combine the hippo optimization (HO) method with the alternating optimization (AO) method to obtain a suboptimal solution efficiently. Simulation results show that the proposed algorithm can significantly improve the rate performance of the NOMA system compared to the conventional FPA system as well as other benchmark schemes.

Movable antennas (MAs) have shown significant potential in improving the performance of integrated sensing and communication (ISAC) systems. However, their application in integrated and cost-effective full-duplex (FD) monostatic systems remains underexplored. To bridge this research gap, we develop an MA-ISAC model within an FD monostatic framework, where the self-interference channel is modeled as a function of the antenna position vectors under the near-field channel condition. This model enables antenna position optimization for maximizing the weighted sum of communication capacity and sensing mutual information. The resulting optimization problem is non-convex making it challenging to solve optimally. To address this, we employ the fractional programming (FP) method and propose an alternating optimization (AO) algorithm that jointly optimizes the beamforming and antenna positions at the transceivers. Specifically, closed-form solutions for the transmit and receive beamforming matrices are derived using the Karush-Kuhn-Tucker (KKT) conditions, and a novel coarse-to-fine grained searching (CFGS) approach is used to determine high-quality sub-optimal antenna positions. Numerical results demonstrate that with strong self-interference cancellation (SIC) capabilities, MAs significantly enhance the overall performance and reliability of the ISAC system when utilizing our proposed algorithm, compared to conventional fixed-position antenna designs.

In this paper, we develop a six-dimensional movable antenna (6DMA) enhanced multi-access point (AP) coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP's antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and can attain considerable performance improvement compared to the conventional fixed antenna (FA)-based schemes.

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing power-measurement-based IRS reflection designs in wideband channels.

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, an upper bound on the minimum signal-to-interference-plus-noise ratio (SINR) across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. Moreover, we further explore the MA design strategy based on statistical channel state information (CSI), with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bound and also outperform the benchmark schemes using dense or sparse arrays with FPAs.

Movable antenna (MA) has been deemed as a promising technology to flexibly reconfigure wireless channels by adjusting the antenna positions in a given local region. In this paper, we investigate the application of the MA technology in both decode-and-forward (DF) and amplify-and-forward (AF) relaying systems, where a relay is equipped with multiple MAs to assist in the data transmission between two single-antenna nodes. For the DF relaying system, our objective is to maximize the achievable rate at the destination by jointly optimizing the positions of the MAs in two stages for receiving signals from the source and transmitting signals to the destination, respectively. To drive essential insights, we first derive a closed-form upper bound on the maximum achievable rate of the DF relaying system. Then, a low-complexity algorithm based on projected gradient ascent (PGA) and alternating optimization (AO) is proposed to solve the antenna position optimization problem. For the AF relaying system, our objective is to maximize the achievable rate by jointly optimizing the two-stage MA positions as well as the AF beamforming matrix at the relay, which results in a more challenging optimization problem due to the intricate coupling variables. To tackle this challenge, we first reveal the hidden separability among the antenna position optimization in the two stages and the beamforming optimization. Based on such separability, we derive a closed-form upper bound on the maximum achievable rate of the AF relaying system and propose a low-complexity algorithm to obtain a high-quality suboptimal solution to the considered problem. Simulation results validate the efficacy of our theoretical analysis and demonstrate the superiority of the MA-enhanced relaying systems to the conventional relaying systems with fixed-position antennas (FPAs) and other benchmark schemes.

This paper investigates movable antenna (MA) empowered secure transmission in near-field multiple-input multiple-output (MIMO) communication systems, where the base station (BS) equipped with an MA array transmits confidential information to a legitimate user under the threat of a potential eavesdropper. To enhance physical layer security (PLS) of the considered system, we aim to maximize the secrecy rate by jointly designing the hybrid digital and analog beamformers, as well as the positions of MAs at the BS. To solve the formulated non-convex problem with highly coupled variables, an alternating optimization (AO)-based algorithm is introduced by decoupling the original problem into two separate subproblems. Specifically, for the subproblem of designing hybrid beamformers, a semi-closed-form solution for the fully-digital beamformer is first derived by a weighted minimum mean-square error (WMMSE)-based algorithm. Subsequently, the digital and analog beamformers are determined by approximating the fully-digital beamformer through the manifold optimization (MO) technique. For the MA positions design subproblem, we utilize the majorization-minimization (MM) algorithm to iteratively optimize each MA’s position while keeping others fixed. Extensive simulation results validate the considerable benefits of the proposed MA-aided near-field beam focusing approach in enhancing security performance compared to the traditional far-field and/or the fixed position antenna (FPA)-based systems. In addition, the proposed scheme can realize secure transmission even if the eavesdropper is located in the same direction as the user and closer to the BS.

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, we extend the field response channel model for MA systems to the near-field propagation scenario. Next, we examine MA-aided multiuser communication systems under both digital and analog beamforming architectures. For digital beamforming, spatial division multiple access (SDMA) is utilized, where an upper bound on the minimum achievable rate across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. For analog beamforming, orthogonal frequency division multiple access (OFDMA) is employed, and an upper bound on the minimum achievable rate among users is also derived. An alternating optimization (AO) algorithm is proposed to iteratively optimize the APV, analog beamforming vector (ABFV), and power allocation until convergence. For both architectures, we further explore MA design strategies based on statistical channels, with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bounds and also outperform the benchmark schemes using dense or sparse arrays with FPAs.

In this paper, we study the intelligent reflecting surface (IRS) deployment problem where a number of IRSs are optimally placed in a target area to improve its signal coverage with the serving base station (BS). To achieve this, we assume that there is a given set of candidate sites in the target area for IRS deployment and divide the area into multiple grids of identical size. Then, we derive the average channel power gains from the BS to the IRS at each candidate site and from this IRS to any grid in the target area in terms of IRS parameters, including its size, position, height, and orientation. Thus, we are able to approximate the average cascaded channel power gain from the BS to each grid via any IRS, introducing an effective IRS reflection gain based on the large-scale channel knowledge only. Next, we formulate a multi-IRS deployment optimization problem to minimize the total deployment cost by selecting a subset of candidate sites for deploying IRSs and jointly optimizing their heights, orientations, and numbers of reflecting elements while satisfying a given coverage rate performance requirement over all grids in the target area. To solve this challenging combinatorial optimization problem, we first reformulate it as an integer linear programming problem and solve it optimally using the branchand- bound (BB) algorithm. In addition, we propose an efficient successive refinement algorithm to further reduce computational complexity. Simulation results demonstrate that the proposed lower-complexity successive refinement algorithm achieves near-optimal performance but with significantly reduced running time compared to the proposed optimal BB algorithm, as well as superior performance-cost trade-off compared to other baseline IRS deployment strategies.

Integrated sensing and communication (ISAC) systems have the issue of secrecy leakage when using the ISAC waveforms for sensing, thus posing a potential risk for eavesdropping. To address this problem, we propose to employ movable antennas (MAs) and reconfigurable intelligent surface (RIS) to enhance the physical layer security (PLS) performance of ISAC systems, where an eavesdropping target potentially wiretaps the signals transmitted by the base station (BS). To evaluate the synergistic performance gain provided by MAs and RIS, we formulate an optimization problem for maximizing the sum-rate of the users by jointly optimizing the transmit/receive beamformers of the BS, the reflection coefficients of the RIS, and the positions of MAs at communication users, subject to a minimum communication rate requirement for each user, a minimum radar sensing requirement, and a maximum secrecy leakage to the eavesdropping target. To solve this non-convex problem with highly coupled variables, a two-layer penalty-based algorithm is developed by updating the penalty parameter in the outer-layer iterations to achieve a trade-off between the optimality and feasibility of the solution. In the inner-layer iterations, the auxiliary variables are first obtained with semi-closed-form solutions using Lagrange duality. Then, the receive beamformer filter at the BS is optimized by solving a Rayleigh-quotient subproblem. Subsequently, the transmit beamformer matrix is obtained by solving a convex subproblem. Finally, the majorization-minimization (MM) algorithm is employed to optimize the RIS reflection coefficients and the positions of MAs. Extensive simulation results validate the considerable benefits of the proposed MAs-aided RIS-ISAC systems in enhancing security performance compared to traditional fixed position antenna (FPA)-based systems.

Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user’s reference signal received power (RSRP) measurements with time-varying random IRS training reflections. As RSRP is readily accessible in existing communication systems, our proposed channel estimation method does not require additional pilot transmission in IRS-aided wideband communication systems. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS reflection designs based on user power measurements.

Channel gain map (CGM) contains crucial large-scale fading information regarding wireless channels at specific frequency bands in wireless networks. Traditional CGM construction methods require either detailed propagation environment information or numerous channel measurements, rendering them practically cumbersome to implement. To overcome such difficulties, we propose in this paper a novel scatterer-based CGM construction framework to characterize the large-scale channel fading in a spatial region by estimating the scatterer responses from a few channel gain measurements at designated locations in the region. In particular, the region is divided into equally-spaced grids so as to smooth out the small-scale fading by averaging the channel power gain within each grid. As the average channel power gain within each grid can be expressed as a function of large-scale multi-path channel parameters such as the scatterer response coefficients and path-loss coefficients, an iterative algorithm is proposed to estimate these parameters based on only a sufficient number of channel gain measurements taken at random locations around each scatterer. Specifically, the proposed algorithm decomposes the parameter estimation problem into a set of univariate or linear estimation subproblems, which can be efficiently solved by one-dimensional (1D) line search and least-squares methods. Based on the estimated channel parameters, the CGM over the whole region can be estimated by calculating the average channel power gain for each grid. Simulation results under both the two-dimensional (2D) multipath channel model and the practical three-dimensional (3D) ray-tracing channel model verify that the proposed scatterer-based channel gain characterization can accurately capture the large-scale channel gain spatial distribution within a region, and the proposed CGM estimation framework outperforms other channel-measurement-based benchmarks in terms of estimation accuracy, while requiring a significantly reduced number of measurements.

By reconfiguring wireless channels via passive signal reflection, intelligent reflecting surface (IRS) can bring significant performance enhancement for wireless communication systems. However, such performance improvement generally relies on the knowledge of channel state information (CSI) for IRS-involved links. Prior works on IRS CSI acquisition mainly estimate IRS-cascaded channels based on the extra pilot signals received at the users/base station (BS) with time-varying IRS reflections, which, however, needs to modify the existing channel training/estimation protocols of wireless systems. To address this issue, we propose in this paper a new channel estimation scheme for IRS-assisted communication systems based on the received signal power measured at the user terminal, which is practically attainable without the need of changing the current protocol. Due to the lack of signal phase information in measured power, the autocorrelation matrix of the BS-IRS-user cascaded channel is estimated by solving an equivalent rank-minimization problem. To this end, a low-rank-approaching (LRA) algorithm is proposed by employing the fractional programming and alternating optimization techniques. To reduce computational complexity, an approximate LRA (ALRA) algorithm is also developed. Furthermore, these two algorithms are extended to be robust against the receiver noise and quantization error in power measurement. Simulation results are provided to verify the effectiveness of the proposed channel estimation algorithms as well as the IRS passive reflection design based on the estimated channel autocorrelation matrix.

The low-earth orbit (LEO) satellite network has been recognized as a promising technology to enable the ubiquitous coverage and massive connectivity for future sixth-generation (6G) mobile communications. Due to the ultra-dense constellation, efficient beam coverage and interference mitigation are crucial to LEO satellite communication systems, while the conventional directional antennas and fixed-position antenna (FPA) arrays both have limited degrees of freedom (DoFs) in beamforming to adapt to the time-varying coverage requirement of terrestrial users. To address this challenge, we propose in this paper utilizing movable antenna (MA) arrays to enhance the satellite beam coverage and interference mitigation. Specifically, given the satellite orbit and the coverage requirement within a specific time interval, the antenna position vector (APV) and antenna weight vector (AWV) of the satellite-mounted MA array are jointly optimized over time to minimize the average signal leakage power to the interference area of the satellite, subject to the constraints of the minimum beamforming gain over the coverage area, the continuous movement of MAs, and the constant modulus of AWV. The corresponding continuous-time decision process for the APV and AWV is first transformed into a more tractable discrete-time optimization problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AWV, where the successive convex approximation (SCA) technique is utilized to obtain locally optimal solutions during the iterations. Moreover, to further reduce the antenna movement overhead, a low-complexity MA scheme is proposed by using an optimized common APV over all time slots. Simulation results validate that the proposed MA array-aided beam coverage schemes can significantly decrease the interference leakage of the satellite compared to conventional FPA-based schemes, while the low-complexity MA scheme can achieve a performance comparable to the continuous-movement scheme.

In this paper, we propose a new wireless sensing system equipped with the movable-antenna (MA) array for improving the sensing performance. First, we show that the angle estimation performance in wireless sensing is fundamentally determined by the array geometry, where the Cramer-Rao bound (CRB) of the mean square error (MSE) for angle of arrival (AoA) estimation is derived as a function of the MAs’ positions. Then, we aim to achieve the minimum of maximum (min-max) CRBs of estimation MSE for the two AoAs with respect to the horizontal and vertical axes, respectively. In particular, for the special case of circular antenna movement region, an optimal solution for the MAs’ positions is derived under certain numbers of MAs and circle radii. Thereby, both the lower- and upper-bounds of the min-max CRB are obtained for the antenna movement region with arbitrary shapes. Moreover, we develop an efficient alternating optimization algorithm to obtain a locally optimal solution for MAs’ positions by iteratively optimizing one between their horizontal and vertical coordinates with the other being fixed. Numerical results demonstrate that our proposed MA arrays can significantly decrease the CRB of AoA estimation MSE as well as the actual MSE compared to conventional uniform planar arrays (UPAs) with different values of uniform inter-antenna spacing.

In this paper, we propose utilizing movable antenna (MA) arrays to enhance the low-earth orbit (LEO) satellite beam coverage and interference mitigation. Specifically, given the satellite orbit and the coverage requirement within a specific time interval, the antenna position vector (APV) and antenna weight vector (AWV) of the satellite-mounted MA array are jointly optimized over time to minimize the average signal leakage power to the interference area of the satellite, subject to the constraints of the minimum beamforming gain over the coverage area, the continuous movement of MAs, and the constant modulus of AWV. The corresponding continuous-time decision process for the APV and AWV is first transformed into a more tractable discrete-time optimization problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AWV, where the successive convex approximation (SCA) technique is utilized to obtain locally optimal solutions during the iterations. Simulation results validate that the proposed MA array-aided beam coverage scheme can significantly decrease the interference leakage of the satellite compared to conventional fixed-position antenna (FPA)-based schemes.

In this paper, we investigate the movable antenna (MA)-aided wideband communications employing orthogonal frequency division multiplexing (OFDM) transmissions. Under the general multi-tap field-response channel model, the wireless chan-nel variations in both space and frequency are characterized with different positions of the MAs at the transmitter (Tx) and receiver (Rx) sides. We reveal that the MA positioning can balance between the amplitude and phase over different channel taps. Then, an upper bound on the OFDM achievable rate is derived in closed form when the size of the TxlRx region for antenna movement can be arbitrarily large. Furthermore, we develop a parallel greedy ascent (PGA) algorithm to obtain locally optimal solutions to the MAs' positions for OFDM rate maximization subject to finite-size TxlRx regions. Simulation results demonstrate that the proposed algorithm closely approaches the OFDM rate upper bound with the increase of TxlRx region sizes and outperforms the conventional system with fixed-position antennas (FPAs).

This paper proposes a general channel estimation framework for movable antenna (MA) communication systems. In this framework, the channel state information between the entire transmitter (Tx) and receive (Rx) regions can be re-constructed, so as to find the optimal positions of the MAs for reaping performance gains. Specifically, the field-response channel structure is utilized to represent the channel response in terms of the angles of departure (AoDs), angles of arrival (AoAs), and complex coefficients of the multi-path components (MPCs). Then, the compressed sensing method is employed to jointly estimate the MPC information, i.e., the AoDs, AoAs, and complex coefficients of the paths, with a limited number of channel measurements. Notably, the measurement matrix under the proposed framework is fundamentally determined by the Tx-MA and Rx-MA measurement positions, which further affects the channel estimation performance. In this regard, four MA measurement position setups are proposed, and the channel estimation performance of each setup is further compared. Finally, simulation results show that the complete CSI between the entire Tx and Rx regions can be reconstructed by our proposed channel estimation framework with a high accuracy.

Due to the inherent characteristics of system extensibility and implementation flexibility, unmanned aerial vehicle (UAV)-assisted data aggregations will play an essential role in Internet of Things (IoT) networks, where both communication security and timeliness are of high priority. In this paper, we study uplink data aggregation in cooperative jamming-aided large-scale UAV-IoT networks under the threat of eavesdroppers. By employing stochastic geometry, we derive performance metrics related to the age of information (AoI) and secrecy outage probability (SOP) in a system-level manner, and formulate the combat between legitimate entities (i.e., IoT devices and cooperative jammers) and eavesdroppers as a two-stage Stackelberg game. To solve the formulated game, the backward induction method is utilized to obtain the Stackelberg equilibrium (SE) iteratively. Specifically, we first obtain the minimum detection error probability for the eavesdropper by optimizing its detection threshold using the successive convex approximation (SCA) technique. Subsequently, the minimization of AoI violation probability and SOP for the legitimate entity is achieved using the proposed tighter α branch and bound (T-αBB) method by jointly optimizing the transmit powers of the typical IoT device and cooperative jammers as well as the deployment altitude of the typical UAV. Extensive numerical results demonstrate that the proposed solution converges rapidly, with the timeliness and secrecy metrics decreasing by 25.1%, 33.1%, 35.9%, and 37.6% compared to the benchmark scheme in suburban, urban, dense urban, and high-rise urban environments, respectively.

Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.

This paper considers an unmanned aerial vehicle (UAV)-assisted communication system for data collection in urban areas, where multiple UAVs are dispatched to harvest data from multiple ground user equipments (UEs). We adopt a blockage-aware channel model to characterize the practical blockage effects for air-to-ground (A2G) links caused by buildings. Aiming to minimize the mission completion time while satisfying the data collection requirements of UEs, we formulate a problem by jointly optimizing the UAV three-dimensional (3-D) trajectory and resource allocation, including the UE scheduling and subcarrier assignment. To solve the formulated non-convex combinatorial programming problem, we propose a suboptimal algorithm that solves two subproblems iteratively. Specifically, in each iteration, the trajectory design subproblem jointly optimizes the UAVs’ waypoints and time slot length to decrease the mission completion time, which is solved by employing block successive convex approximation (BSCA). For the resource allocation subproblem, we develop a heuristic algorithm for UE scheduling and subcarrier assignment to increase the collected data volume for a given time duration. Simulation results demonstrate the superior performance of the proposed algorithm in terms of mission completion time compared to benchmark schemes.

Intelligent reflecting surface (IRS) has emerged as a promising technique to control wireless propagation wireless environment for improving the communication performance cost-effectively and extending the wireless signal coverage of access point (AP). In order to reduce the path-loss of the cascaded user-IRS-AP channels, the IRS-integrated AP architecture has been proposed to deploy the antenna array of the AP and the IRSs within the same antenna radome. To reduce the pilot overhead for estimating all IRS-involved channels, in this paper, we propose a novel codebook-based IRS reflection design for the IRS-integrated AP to enhance the coverage performance in a given area. In particular, the codebook consisting of a small number of codewords is designed offline by employing an efficient sector division strategy based on the azimuth angle. To ensure the performance of each sector, we optimize its corresponding codeword for IRS reflection pattern to maximize the sector-min-average-effective-channel-power (SMAECP) by applying the alternating optimization (AO) and semidefinite relaxation (SDR) methods. With the designed codebook, the AP performs the IRS reflection training by sequentially applying all codewords and selects the one achieving the best communication performance for data transmission. Numerical results show that our proposed codebook design can enhance the average channel power of the whole coverage area, as compared to the system without IRS. Moreover, the proposed codebook-based IRS reflection design is compared with several benchmark schemes, which achieves significant performance gain in both single-user and multi-user transmissions.

In this paper, we propose a new multiple-input multiple-output (MIMO) communication system with movable antennas (MAs) to exploit the antenna position optimization for enhancing the capacity. Different from conventional MIMO systems with fixed-position antennas (FPAs), the proposed system can flexibly change the positions of transmit/receive MAs, such that the MIMO channel between them is reconfigured to achieve higher capacity. We aim to characterize the capacity of MA-enabled point-to-point MIMO communication systems, by jointly optimizing the positions of transmit and receive MAs as well as the covariance of transmit signals. First, we develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix and the position of each transmit/receive MA with the other variables being fixed. Next, we propose alternative algorithms of lower complexity for capacity maximization in the low-SNR regime and for the multiple-input single-output (MISO) and single-input multiple-output (SIMO) cases. Numerical results show that our proposed MA systems significantly improve the MIMO channel capacity compared to traditional FPA systems as well as various benchmark schemes, and useful insights are drawn into the capacity gains of MA systems.

In this paper, we propose a new wireless sensing system equipped with the movable-antenna (MA) array, which can flexibly adjust the positions of antenna elements for improving the sensing performance over conventional antenna arrays with fixed-position antennas (FPAs). First, we show that the angle estimation performance in wireless sensing is fundamentally determined by the array geometry, where the Cramer-Rao bound (CRB) of the mean square error (MSE) for angle of arrival (AoA) estimation is derived as a function of the antennas’ positions for both one-dimensional (1D) and two-dimensional (2D) MA arrays. Then, for the case of 1D MA array, we obtain a globally optimal solution for the MAs’ positions in closed form to minimize the CRB of AoA estimation MSE. While in the case of 2D MA array, we aim to achieve the minimum of maximum (min-max) CRBs of estimation MSE for the two AoAs with respect to (w.r.t.) the horizontal and vertical axes, respectively. In particular, for the special case of circular antenna movement region, an optimal solution for the MAs’ positions is derived under certain numbers of MAs and circle radii. Thereby, both the lower- and upper-bounds of the min-max CRB are obtained for the antenna movement region with arbitrary shapes. Moreover, we develop an efficient alternating optimization algorithm to obtain a locally optimal solution for MAs’ positions by iteratively optimizing one between their horizontal and vertical coordinates with the other being fixed. Numerical results demonstrate that our proposed 1D/2D MA arrays can significantly decrease the CRB of AoA estimation MSE as well as the actual MSE compared to conventional uniform linear arrays (ULAs)/uniform planar arrays (UPAs) with different values of uniform inter-antenna spacing. Furthermore, it is revealed that the steering vectors of our designed 1D/2D MA arrays exhibit low correlation in the angular domain, thus effectively reducing the ambiguity of angle estimation.

The codebook-based analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters via low-complexity beam training. However, conventional beamforming codebook design based on array response vectors for narrowband communications may suffer from severe performance loss in wideband systems due to the “beam squint” effect over frequency. To tackle this issue, we propose in this paper a new codebook design method for analog beamforming in wideband THz systems. In particular, to characterize the analog beamforming performance in wideband systems, we propose a new metric termed wideband beam gain, which is given by the minimum beamforming gain over the entire frequency band given a target angle. Based on this metric, a wideband analog beamforming codebook design problem is formulated for optimally balancing the beamforming gains in both the spatial and frequency domains, and the performance loss of conventional narrowband beamforming in wideband systems is analyzed. To solve the new wideband beamforming codebook design problem, we divide the spatial domain into orthogonal angular zones each associated with one beam, thereby decoupling the codebook design into a zone division sub-problem and a set of beamforming optimization sub-problems each for one zone. For the zone division sub-problem, we propose a bisection method to obtain the optimal boundaries for separating adjacent zones. While for each of the per-zone-based beamforming optimization sub-problems, we further propose an efficient augmented Lagrange method (ALM) to solve it. Numerical results demonstrate the performance superiority of our proposed codebook design for wideband analog beamforming to the narrowband beamforming codebook and also validate our performance analysis.

Intelligent reflecting surface (IRS) has been recognized as a transformative technology for enabling smart and reconfigurable radio environment cost-effectively by leveraging its controllable passive reflection. In this paper, we study an IRS-assisted coverage enhancement problem for a given region, aiming to optimize the passive reflection of the IRS for improving the average communication performance in the region by accounting for both deterministic and random channels in the environment. To this end, we first derive the closed-form expression of the average received signal power in terms of the deterministic base station (BS)-IRS-user cascaded channels over all user locations, and propose an IRS-aided coverage enhancement framework to facilitate the estimation of such deterministic channels for IRS passive reflection design. Specifically, to avoid the exorbitant overhead of estimating the cascaded channels at all possible user locations, a location selection method is first proposed to select only a set of typical user locations for channel estimation by exploiting the channel spatial correlation in the region. To estimate the deterministic cascaded channels at the selected user locations, conventional IRS channel estimation methods require additional pilot signals, which not only results in high system training overhead but also may not be compatible with the existing communication protocols. To overcome this issue, we further propose a single-layer neural network (NN)-enabled IRS channel estimation method in this paper, based on only the average received signal power measurements at each selected location corresponding to different IRS random training reflections, which can be offline implemented in current wireless systems. Based on the estimated channels, the IRS passive reflection is then optimized to maximize the average received signal power over the selected locations. Numerical results demonstrate that our proposed scheme can significantly improve the coverage performance of the target region and outperform the existing power-measurement-based IRS reflection designs.

Movable antenna (MA) is a new technology with great potential to improve communication performance by enabling local movement of antennas for pursuing better channel conditions. In particular, the acquisition of complete channel state information (CSI) between the transmitter (Tx) and receiver (Rx) regions is an essential problem for MA systems to reap performance gains. In this paper, we propose a general channel estimation framework for MA systems by exploiting the multi-path field response channel structure. Specifically, the angles of departure (AoDs), angles of arrival (AoAs), and complex coefficients of the multi-path components (MPCs) are jointly estimated by employing the compressed sensing method, based on multiple channel measurements at designated positions of the Tx-MA and Rx-MA. Under this framework, the Tx-MA and Rx-MA measurement positions fundamentally determine the measurement matrix for compressed sensing, of which the mutual coherence is analyzed from the perspective of Fourier transform. Moreover, two criteria for MA measurement positions are provided to guarantee the successful recovery of MPCs. Then, we propose several MA measurement position setups and compare their performance. Finally, comprehensive simulation results show that the proposed framework is able to estimate the complete CSI between the Tx and Rx regions with a high accuracy.

Movable antenna (MA) is an innovative technology that facilitates the repositioning of antennas within the transmitter/receiver area to enhance channel conditions and communication performance. This paper proposes a new base station (BS) architecture employing multiple MAs for improving the multiuser network performance. First, the uplink multiple access channel (MAC) is modeled to capture the characteristics of the variation of wireless channels caused by the movement of MAs at the BS. Subsequently, we propose to maximize the minimum achievable rate among multiple users for MA-aided multiuser uplink transmissions by joint optimization of the MAs’ positions, their receive combining at the BS, and the transmit power of users, subject to the MAs’ positions-related constraints and the maximum transmit power of each user. To tackle this highly non-convex max-min fairness problem, we propose a two-loop iterative algorithm based on the particle swarm optimization (PSO). Specifically, the outer-loop updates the positions of a set of particles, where each particle’s position corresponds to one realization of the antenna position vector (APV) of all MAs. The inner-loop conducts the fitness evaluation for each particle, determining the max-min achievable rate for multiple users based on the current APV. Therein, for given APV, the receive combining matrix at the BS and the transmit power for each user are optimized using the block coordinate descent (BCD) technique. To further reduce the computational complexity, we develop an alternating optimization (AO)-based algorithm via iteratively updating the APV, combining matrix, and transmit power. Finally, extensive simulations demonstrate that the antenna position optimization for MAs-aided BSs can significantly improve the rate performance as compared to conventional BSs with fixed-position antennas (FPAs).

In this paper, we propose to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channels at different locations. Based on the proposed channel model, we formulate the joint optimization problem of UAV three-dimensional (3-D) positioning and resource allocation, by power allocation, user association, and subcarrier allocation, to maximize the minimum achievable rate among users. To solve this non-convex combinatorial programming problem, we introduce a penalty term to relax it and develop a suboptimal solution via a penalty-based double-loop iterative optimization framework. The inner loop solves the penalized problem by employing the block successive convex approximation (BSCA) technique, where the UAV positioning and resource allocation are alternately optimized in each iteration. The outer loop aims to obtain proper penalty multipliers to ensure the solution of the penalized problem converges to that of the original problem. Simulation results demonstrate the superiority of the proposed algorithm over other benchmark schemes in terms of the minimum achievable rate.

Movable antenna (MA) is a promising technology to improve wireless communication performance by varying the antenna position in a given finite area at the transceivers to create more favorable channel conditions. In this paper, we investigate the MA-enhanced multiple-access channel (MAC) for the uplink transmission from multiple users each equipped with a single MA to a base station (BS) with a fixed-position antenna (FPA) array. A field-response based channel model is used to characterize the multi-path channel between the antenna array of the BS and each user’s MA with a flexible position. To evaluate the MAC performance gain provided by MAs, we formulate an optimization problem for minimizing the total transmit power of users, subject to a minimum-achievable-rate requirement for each user, where the positions of MAs and the transmit powers of users, as well as the receive combining matrix of the BS are jointly optimized. To solve this non-convex optimization problem involving intricately coupled variables, we develop two algorithms based on zero-forcing (ZF) and minimum mean square error (MMSE) combining methods, respectively. Specifically, for each algorithm, the combining matrix of the BS and the total transmit power of users are expressed as a function of the MAs’ position vectors, which are then optimized by using the proposed multi-directional descent (MDD) framework. It is shown that the proposed ZF-based and MMSE-based MDD algorithms can converge to high-quality suboptimal solutions with low computational complexities. Simulation results demonstrate that the proposed solutions for MA-enhanced multiple access systems can significantly decrease the total transmit power of users as compared to conventional FPA systems employing antenna selection under both perfect and imperfect field-response information.

Movable antenna (MA) has emerged as a promising technology to enhance wireless communication performance by enabling the local movement of antennas at the transmitter (Tx) and/or receiver (Rx) for achieving more favorable channel conditions. As the existing studies on MA-aided wireless communications have mainly considered narrow-band transmission in flat fading channels, we investigate in this paper the MA-aided wideband communications employing orthogonal frequency division multiplexing (OFDM) in frequency-selective fading channels. Under the general multi-tap field-response channel model, the wireless channel variations in both space and frequency are characterized with different positions of the MAs. Unlike the narrow-band transmission where the optimal MA position at the Tx/Rx simply maximizes the single-tap channel amplitude, the MA position in the wideband case needs to balance the amplitudes and phases over multiple channel taps in order to maximize the OFDM transmission rate over multiple frequency subcarriers. First, we derive an upper bound on the OFDM achievable rate in closed form when the size of the Tx/Rx region for antenna movement is arbitrarily large. Next, we develop a parallel greedy ascent (PGA) algorithm to obtain locally optimal solutions to the MAs’ positions for OFDM rate maximization subject to finite-size Tx/Rx regions. To reduce computational complexity, a simplified PGA algorithm is also provided to optimize the MAs’ positions more efficiently. Simulation results demonstrate that the proposed PGA algorithms can approach the OFDM rate upper bound closely with the increase of Tx/Rx region sizes and outperform conventional systems with fixed-position antennas (FPAs) under the wideband channel setup.

In this paper, we propose a novel antenna architecture called movable antenna (MA) to improve the performance of wireless communication systems. Different from conventional fixed-position antennas (FPAs) that undergo random wireless channel variation, the MAs with the capability of flexible movement can be deployed at positions with more favorable channel conditions to achieve higher spatial diversity gains. To characterize the general multi-path channel in a given region or field where the MAs are deployed, a field-response model is developed by leveraging the amplitude, phase, and angle of arrival/angle of departure (AoA/AoD) information on each of the multiple channel paths under the far-field condition. Based on this model, we then analyze the maximum channel gain achieved by a single receive MA as compared to its FPA counterpart in both deterministic and stochastic channels. First, in the deterministic channel case, we show the periodic behavior of the multi-path channel gain in a given spatial field, which can be exploited for analyzing the maximum channel gain of the MA. Next, in the case of stochastic channels, the expected value of an upper bound on the maximum channel gain of the MA in an infinitely large receive region is derived for different numbers of channel paths. The approximate cumulative distribution function (CDF) for the maximum channel gain is also obtained in closed form, which is useful to evaluate the outage probability of the MA system. Moreover, our results reveal that higher performance gains by the MA over the FPA can be acquired when the number of channel paths increases due to more pronounced small-scale fading effects in the spatial domain. Numerical examples are presented which validate our analytical results and demonstrate that the MA system can reap considerable performance gains over the conventional FPA systems with/without antenna selection (AS), and even achieve comparable performance to the single-input multiple-output (SIMO) beamforming system.

One main challenge for implementing intelligent reflecting surface (IRS) aided communications lies in the difficulty to obtain the channel knowledge for the base station (BS)-IRS-user cascaded links, which is needed to design high-performance IRS reflection in practice. Traditional methods for estimating IRS cascaded channels are usually based on the additional pilot signals received at the BS/users, which increase the system training overhead and also may not be compatible with the current communication protocols. To tackle this challenge, we propose in this paper a new single-layer neural network (NN)-enabled IRS channel estimation method based on only the knowledge of users' individual received signal power measurements corresponding to different IRS random training reflections, which are easily accessible in current wireless systems. To evaluate the effectiveness of the proposed channel estimation method, we design the IRS reflection for data transmission based on the estimated cascaded channels in an IRS-aided multiuser communication system. Numerical results show that the proposed IRS channel estimation and reflection design can significantly improve the minimum received signal-to-noise ratio (SNR) among all users, as compared to existing power measurement based designs.

This paper proposes a novel antenna architecture called movable antenna (MA) to improve the performance of wireless communication systems. With thecapability of flexiblemovement, MAs can be deployed at positions with more favorable channel conditions to achieve higher spatial diversity gains. Based on our proposed field-response channel model, we analyze the maximum channel gain achieved by a single receive MA as compared to its fixed-position antenna (FPA) counterpart in both deterministic and stochastic channels. In the deterministic channel case, we show the periodic behavior of the multi-path channel gain in a given spatial field. In the stochastic channels, the expected value and the approximate cumulative distribution function of an upper bound on the maximum channel gain of the MA are derived. Numerical examples validate our analytical results and demonstrate that a single MA can reap considerable performance gains over the conventional FPA systems with/without antenna selection, and even achieve comparable performance to the single-input multiple-output beamforming system.

In this paper, we propose a new multiple-input multiple-output (MIMO) communication system with movable antennas (MAs) to exploit more degrees of freedom (DoFs) in the spatial domain for enhancing the capacity. Different from conventional MIMO systems with fixed-position antennas (FPAs), the proposed system can flexibly change the positions of MAs, such that the MIMO channel between the transmit and receive antennas is reconfigured for achieving higher capacity. We aim to achieve the maximum capacity of MA-enabled point-to-point MIMO communication systems, by jointly optimizing the positions of the receive MAs and the covariance of the transmit signals. We develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix and the position of each MA with the other variables being fixed. Numerical results show that our proposed scheme substantially improves the MIMO capacity compared to traditional FPA systems.

This paper proposes to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users collaboratively in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channel at different locations. Based on the proposed channel model, we formulate a joint design problem of UAV three-dimensional (3-D) positioning and resource allocation, including the user association and subcarrier allocation, to maximize the minimum achievable rate among users. We propose a suboptimal iterative algorithm to solve the mixed-integer non-convex optimization problem. Specifically, the UAV positioning and resource allocation are alternately optimized in each iteration by employing the successive convex approximation (SCA) and matching theory, respectively. Simulation results reveal that the proposed algorithm outperforms several benchmark schemes in terms of the minimum achievable rate.

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

Due to the advantage of achieving scalable connectivity and low-latency communications, air-ground integrated mesh networks (AGIMNs) will play an important role in the next generation wireless communication systems. However, due to the heterogeneous character of AGIMNs, it is challenging to manage the network and optimize the communication resources for improving the end-to-end (E2E) performance. Therefore, in this paper, we study a joint routing and time-frequency resource scheduling problem aiming at minimizing the total weighted E2E delay for heterogeneous AGIMNs. To capture the features of this complex system, we mathematically model the network constraints and formulate an optimization problem. To solve the original nonconvex problem, a suboptimal solution is proposed. First, we propose an optimal minimum-weight routing method, in which the hop count, conflict delay, and contention delay are taken into consideration. Then, we transform the time-frequency resource scheduling subproblem into a series of tractable problems for maximizing the number of active links through channel assignment in successive time slots. Finally, the successive convex approximation (SCA) technique is utilized to solve the channel assignment problem per time slot. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmarks in terms of the total E2E delay.

In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we first model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confirm that the proposed solution significantly outperforms three benchmark schemes and achieves a performance close to the upper bound on the UAV relay system.

In this paper, we propose to employ multiple unmanned aerial vehicle (UAV) base stations to serve ground users in the millimeter-wave (mmWave) frequency bands. To improve the spectrum efficiency, uniform planar arrays are equipped at the UAVs and users for compensation of the high path loss and for mitigation of interference. We formulate a problem to jointly optimize the UAV positioning, user clustering, and hybrid analog-digital beamforming (BF) for the maximization of user achievable sum rate (ASR), subject to a minimum rate constraint for each user. Since the problem is highly non-convex and involves high-dimensional variable matrices and combinatorial programming variables, we develop a suboptimal solution via alternating optimization, successive convex optimization, and combinatorial optimization. First, we design the UAV positioning and user clustering under the assumption of ideal beam patterns, which significantly decouples the UAV positioning and directional BF. Then, the transmit and receive BF variables are successively optimized to approach the ideal beam patterns. Our simulation results verify the convergence and superiority of the proposed algorithm. Significant performance gains can be obtained compared to some benchmark schemes in terms of the ASR, and the proposed hybrid BF solution closely approaches a performance bound given by fully-digital BF.

As the commercial launch of the fifth-generation (5G) wireless communications gets near, the trend from the Internet of Things (IoT) to the Internet of Everything (IoE) is emerging. Due to the advantages of the high mobility, high Line-of-Sight (LoS) probability and low labor cost, unmanned aerial vehicles (UAVs) may play an important role in the future IoT communication networks, e.g., data collection in remote areas. In this article, we study the 3-D placement and resource allocation of multiple UAV-mounted base stations (BSs) in an uplink IoT network, where the balanced task for the UAV-BSs, the limited channel resource, and the signal interference are taken into consideration. In the considered system, the total transmission power of IoT devices is minimized, subject to a signal-to-interference-and-noise ratio (SINR) threshold for each device. First, aiming to balance the task of each UAV, we propose a clustering algorithm based on an improved$K$-means method to divide IoT devices into several groups so that the number of devices in each group is roughly the same. Then, based on matching theory, a modified-Hungarian-based dynamic many–many matching (HD4M) algorithm is designed for assigning subchannels to IoT devices, which can efficiently mitigate the interference. Finally, we jointly optimize the transmission power of IoT devices and the altitudes of UAVs via an alternating iterative method. The simulation results show that the total transmission power decreases significantly after applying the proposed algorithms.

Recently, unmanned aerial vehicles (UAVs) have attracted lots of attention because of their high mobility and low cost. This article investigates a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage rate by optimizing the three-dimensional (3D) positions of UAVs, user clustering, and frequency band allocation. Compared with the existing works, the constraints of the required quality of service (QoS) and the service ability of each UAV are considered, which makes the problem more challenging. A three-step method is developed to solve the formulated mixed-integer programming problem. First, to ensure that each UAV can serve more number of users, the maximum service radius of UAVs is derived according to the required minimum power of the received signals for the users. Second, an algorithm based on artificial bee colony (ABC) algorithm is proposed to minimize the number of required UAVs. Third, the 3D position and the frequency band of each UAV are designed to increase the power of the target signals and to reduce the interference. Finally, simulation results are presented to demonstrate the superiority of the proposed solution for UAV-assisted communication systems.

In this paper, we investigate millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) networks with multiple unmanned aerial vehicle (UAV) mounted base stations (BSs). Uniform planar arrays are equipped at the UAV-BSs to perform hybrid analog-digital beamforming (BF) for compensation of the high path loss of mmWave channels and for mitigation of intra-cell and/or inter-cell interference. We jointly optimize the UAV-BS positioning, user assignment, and hybrid BF for maximization of the achievable sum rate (ASR) of the users, subject to a minimum rate constraint for each user. A sub-optimal solution for the resulting high-dimensional and non-convex problem is developed by exploiting alternating optimization, successive convex optimization, and combinatorial optimization. Our simulation results verify the convergence of the proposed algorithm and demonstrate significant performance gains compared to two benchmark schemes in terms of the ASR.

In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is employed to increase the communication capacity of millimeter-wave (mmWave) networks. Large antenna arrays are equipped at the source node (SN), destination node (DN), and FD-UAV relay to overcome the high path loss of mmWave channels and to help mitigate the self-interference at the FD-UAV relay. Specifically, we formulate a problem for maximization of the achievable rate from the SN to the DN, where the UAV position, analog beamforming, and power control are jointly optimized. Since the problem is highly non-convex and involves high-dimensional, highly coupled variable vectors, we first obtain the conditional optimal position of the FD-UAV relay for maximization of an approximate upper bound on the achievable rate in closed form, under the assumption of a line-of-sight (LoS) environment and ideal beamforming. Then, the UAV is deployed to the position which is closest to the conditional optimal position and yields LoS paths for both air-to-ground links. Subsequently, we propose an alternating interference suppression (AIS) algorithm for the joint design of the beamforming vectors and the power control variables. In each iteration, the beamforming vectors are optimized for maximization of the beamforming gains of the target signals and the successive reduction of the interference, where the optimal power control variables are obtained in closed form. Our simulation results confirm the superiority of the proposed positioning, beamforming, and power control method compared to three benchmark schemes. Furthermore, our results show that the proposed solution closely approaches a performance upper bound for mmWave FD-UAV systems.

In this paper, we investigate the combination of non-orthogonal multiple access and millimeter-wave communications (mmWave-NOMA). A downlink cellular system is considered, where an analog phased array is equipped at both the base station and users. A joint Tx-Rx beamforming and power allocation problem is formulated to maximize the achievable sum rate (ASR) subject to a minimum rate constraint for each user. As the problem is non-convex, we propose a sub-optimal solution with three stages. In the first stage, the optimal power allocation with a closed form is obtained for an arbitrary fixed Tx-Rx beamforming. In the second stage, the optimal Rx beamforming with a closed form is designed for an arbitrary fixed Tx beamforming. In the third stage, the original joint Tx-Rx beamforming and power allocation problem is reduced to a Tx beamforming problem by using the previous results, and a boundary-compressed particle swarm optimization (BC-PSO) algorithm is proposed to obtain a sub-optimal solution. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results show that the proposed sub-optimal solution can achieve a significantly better performance in terms of ASR compared with those of the state-of-the-art schemes and the conventional mmWave orthogonal multiple access (mmWave-OMA) system.

The integration of non-orthogonal multiple access in millimeter-Wave communications (mm Wave-NOMA) can significantly improve the spectrum efficiency and increase the number of users in the fifth-generation (5G) mobile communication and beyond. In this paper, we consider a downlink mm Wave-NOMA cellular system, where the base station is mounted with an analog beamforming phased array, and multiple users are served in the same time-frequency resource block. To guarantee user fairness, we formulate joint beamforming and power allocation problem to maximize the minimal achievable rate among the users, i.e., we adopt the max–min fairness. As the problem is difficult to solve due to the non-convex formulation and high dimension of the optimization variables, we propose a sub-optimal solution, which makes use of the spatial sparsity in the angle domain of the mm Wave channel. In the solution, the closed-form optimal power allocation is obtained first, which reduces the joint optimization problem into an equivalent beamforming problem. Then, an appropriate beamforming vector is designed. The simulation results show that the proposed solution can achieve a near-upper-bound performance in terms of achievable rate, which is significantly better than that of the conventional mm Wave orthogonal multiple access (mm Wave-OMA) system.

This paper investigates the application of non-orthogonal multiple access in millimeter-Wave communications (mmWave-NOMA). Particularly, we consider downlink transmission with a hybrid beamforming structure. A user grouping algorithm is first proposed according to the channel correlations of the users. Whereafter, a joint hybrid beamforming and power allocation problem is formulated to maximize the achievable sum rate, subject to a minimum rate constraint for each user. To solve this non-convex problem with high-dimensional variables, we first obtain the solution of power allocation under arbitrary fixed hybrid beamforming, which is divided into intra-group power allocation and inter-group power allocation. Then, given arbitrary fixed analog beamforming, we utilize the approximate zero-forcing method to design the digital beamforming to minimize the inter-group interference. Finally, the analog beamforming problem with the constant-modulus constraint is solved with a proposed boundary-compressed particle swarm optimization algorithm. The simulation results show that the proposed joint approach, including user grouping, hybrid beamforming and power allocation, outperforms the state-of-the-art schemes and the conventional mmWave orthogonal multiple access system in terms of achievable sum rate, and energy efficiency.

In this paper, we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mm-wave) communications (mm-wave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mm-wave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mm-wave-NOMA to more-user mm-wave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieves close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access.

In this paper, we investigate the combination of two key enabling technologies for the fifth generation wireless mobile communication, namely millimeter-wave (mm-wave) communications and non-orthogonal multiple access (NOMA). In particular, we consider a typical two-user uplink mm-wave-NOMA system, where the base station equips an analog beamforming structure with a single radio-frequency chain and serves two NOMA users. An optimization problem is formulated to maximize the achievable sum rate of the two users while ensuring a minimal rate constraint for each user. The problem turns to be a joint power control and beamforming problem, i.e., we need to find the beamforming vectors to steer to the two users simultaneously subject to an analog beamforming structure, and meanwhile control appropriate power on them. As direct search for the optimal solution of the non-convex problem is too complicated, we propose decomposing the original problem into two sub-problems that are relatively easy to solve: one is a power control and beam gain allocation problem, and the other is an analog beamforming problem under a constant-modulus constraint. The rationale of the proposed solution is verified by extensive simulations, and the performance evaluation results show that the proposed sub-optimal solution achieves a close-to-bound uplink sum-rate performance.