With 6G-enabled Intelligent Internet of Vehicles (IIoV) generating massive amounts of sensory data, traditional deep learning models struggle to capture long-range relationships across different sensor types while preserving privacy. This paper proposes DT-Trans, a privacy-preserving federated learning framework that combines Digital Twin technology with Vision Transformers. Our framework first trains a global perception model on synthetic digital twin data, then fine-tunes it efficiently for real-world vehicles. By grouping vehicles with similar driving patterns and allowing them to collaboratively train personalized model components, DT-Trans achieves significant accuracy improvements while maintaining data privacy. The Twin-Enhanced Vision Transformer (TE-ViT) is introduced as the global perception backbone; it is pre-trained on massive synthetic DT data and then fine-tuned via parameter-efficient LoRA adapters to bridge the domain gap between virtual and physical worlds. The Cluster-Enhanced Decoupled PFL (CD-PFL-Trans) algorithm splits each TE-ViT into (i) a shared Transformer encoder (base layer) and (ii) client-specific Transformer decoder heads (personalized layer). Hierarchical clustering on decoder parameters groups clients with similar traffic patterns, enabling group-wise aggregation without exchanging raw sensory data. DT-Trans outperforms CNN-based FedAvg/FedPer by 9.3%-16.2% mAP on V&PKITTI perception tasks and up to 42.8% accuracy improvement on CINIC-10 classification under severe heterogeneity, while reducing on-device FLOPs by 34 % via Transformer sparsity techniques. Our work advances Transformer architectures for scalable, privacy-preserving perception in IIoV.

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.

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.

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.

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.

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) 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.

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).

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.

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.

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.

Intelligent reflecting surface (IRS) has emerged as a promising technology to reconfigure the radio propagation environment by dynamically controlling wireless signal’s amplitude and/or phase via a large number of reflecting elements. In contrast to the vast literature on studying IRS’s performance gains in wireless communications, we study in this paper a new application of IRS for sensing/localizing targets in wireless networks. Specifically, we propose a newself-sensing IRSarchitecture where the IRS controller is capable of transmitting probing signals that are not only directly reflected by the target (referred to as the direct echo link), but also consecutively reflected by the IRS and then the target (referred to as the IRS-reflected echo link). Moreover, dedicated sensors are installed at the IRS for receiving both the direct and IRS-reflected echo signals from the target, such that the IRS can sense the direction of its nearby target by applying a customized multiple signal classification (MUSIC) algorithm. However, since the angle estimation mean square error (MSE) by the MUSIC algorithm is intractable, we propose to optimize the IRS passive reflection for maximizing the average echo signals’ total power at the IRS sensors and derive the resultant Cramer-Rao bound (CRB) of the angle estimation MSE. Last, numerical results are presented to show the effectiveness of the proposed new IRS sensing architecture and algorithm, as compared to other benchmark sensing systems/algorithms.

Channel estimation and hybrid precoding are considered for multi-user millimeter wave massive multi-input multi-output system. A deep learning compressed sensing (DLCS) channel estimation scheme is proposed. The channel estimation neural network for the DLCS scheme is trained offline using simulated environments to predict the beamspace channel amplitude. Then the channel is reconstructed based on the obtained indices of dominant beamspace channel entries. A deep learning quantized phase (DLQP) hybrid precoder design method is developed after channel estimation. The training hybrid precoding neural network for the DLQP method is obtained offline considering the approximate phase quantization. Then the deployment hybrid precoding neural network (DHPNN) is obtained by replacing the approximate phase quantization with ideal phase quantization and the output of the DHPNN is the analog precoding vector. Finally, the analog precoding matrix is obtained by stacking the analog precoding vectors and the digital precoding matrix is calculated by zero-forcing. Simulation results demonstrate that the DLCS channel estimation scheme outperforms the existing schemes in terms of the normalized mean-squared error and the spectral efficiency, while the DLQP hybrid precoder design method has better spectral efficiency performance than other methods with low phase shifter resolution.

In this paper, we develop two high-resolution channel estimation schemes based on the estimating signal parameters via the rotational invariance techniques (ESPRIT) method for frequency-selective millimeter wave (mmWave) massive MIMO systems. The first scheme is based on two-dimensional ESPRIT (TDE), which includes three stages of pilot transmission. This scheme first estimates the angles of arrival (AoA) and angles of departure (AoD) and then pairs the AoA and AoD. The other scheme reduces the pilot transmission from three stages to two stages and therefore reduces the pilot overhead. It is based on one-dimensional ESPRIT and minimum searching (EMS). It first estimates the AoD of each channel path and then searches the minimum from the identified mainlobe. To guarantee the robust channel estimation performance, we also develop a hybrid precoding and combining matrices design method so that the received signal power keeps almost the same for any AoA and AoD. Finally, we demonstrate that the proposed two schemes outperform the existing channel estimation schemes in terms of computational complexity and performance.

Channel estimation for frequency-selective millimeter wave (mmWave) massive MIMO system is investigated. To overcome the frequency-selective fading, orthogonal frequency division multiplexing (OFDM) is employed. First, the channel structure of frequency-selective channel is analyzed to show that all the OFDM subcarriers share the same angles of arrival (AoA) and angles of departure (AoD). Then a two dimensional ESPRIT (TDE)-based channel estimation scheme is proposed, where the super-resolution estimation of AoA and AoD can be obtained by utilizing the rotation invariance of the channel steering vectors. Finally the AoA and AoD are paired to reconstruct the channel. Simulation results show that the proposed TDE-based channel estimation scheme outperforms the existing schemes at high SNR region.

Beamspace channel estimation for millimeter wave (mmWave) massive MIMO system is investigated. An identity matrix approximation (IA)-based beamspace channel estimation scheme is proposed, including the design of hybrid precoding and combining matrix as well as searching the largest entry of over- sampled beamspace receiving matrix. The design of hybrid combining and hybrid precoding is formulated as two optimization problems. By decoupling the design of analog combining and digital combining, closed-form solutions are obtained. Then an algorithm based on bisection search is proposed to search the largest entry of the over-sampled beamspace receiving matrix. Additionally, computational complexity is compared between the proposed scheme and the existing channel estimation schemes. Simulation results show that the proposed IA-based beamspace channel estimation scheme outperforms the existing schemes.