This paper proposes a new hierarchically tunable six-dimensional movable antenna (HT-6DMA) architecture for base station (BS) in future wireless networks, aiming to improve the performance of both wireless communication and sensing. The HT-6DMA BS consists of multiple antenna arrays that can flexibly move on a spherical surface, with their three-dimensional (3D) positions and 3D rotations/orientations efficiently characterized in the global spherical coordinate system (SCS) and their individual local SCSs, respectively. As a result, the 6DMA system is hierarchically tunable in the sense that each array’s global position and local rotation can be separately adjusted in a sequential manner with the other being fixed, thus greatly reducing their design complexity and improving the achievable performance. In particular, we consider an HT-6DMA BS serving multiple single-antenna users in the uplink communication or sensing potential unmanned aerial vehicles (UAVs)/drones in a given airway area. Specifically, for the communication scenario, we aim to maximize the average sum rate of communication users in the long term by optimizing the positions and rotations of all 6DMA arrays at the BS. For the airway sensing scenario, we maximize the minimum received sensing signal power along the airway by optimizing the 6DMA arrays’ positions and rotations along with the BS’s transmit covariance matrix. Despite that the formulated problems are both non-convex and challenging to solve, we propose efficient solutions to them by exploiting the hierarchical tunability of positions/rotations of 6DMA arrays in our proposed model. Numerical results show that the proposed HT-6DMA design significantly outperforms not only the traditional BS with fixed-position antennas (FPAs), but also the existing 6DMA scheme based on alternating array position/rotation optimization. Furthermore, it is unveiled that the performance gains of HT-6DMA mostly come from the arrays’ global position adjustments on the spherical surface, rather than their local rotation adjustments, which provides a useful guide for implementing 6DMA systems under practical performance-complexity trade-off consideration.

Movable antennas (MAs) have emerged as a promising technology to achieve high data rates in wireless communications by dynamically adjusting their positions to mitigate deep fading within a given region. However, to determine the optimal MA positions, full channel state information (CSI) is required for each position within the transmit/receive movement region, which leads to extremely high channel estimation overhead. To tackle this challenge, this paper proposes a new learning-based approach to predict the optimal positions of multiple transmit MAs in a multiple-input single-output (MISO) system without explicit CSI estimation. Specifically, we show that there exists a clear mapping between the optimal MA positions and the channel power gains from a subset of locations within the transmit region to the receiver. To acquire and leverage this mapping, we train a deep neural network (DNN) via offline supervised learning and then use the pre-trained DNN to determine the optimized MA positions in real-time data transmission, based on partial power measurements within the transmit region only. Numerical results demonstrate that the proposed DNN-based method achieves near-optimal performance and significantly outperforms conventional fixed-position antenna (FPA) systems.

This paper studies a near-field integrated sensing and communication (ISAC) system with extremely large-scale antenna array (ELAA), in which a base station (BS) deployed with a very large number of antennas transmits wireless signals to communicate with multiple communication users (CUs) and simultaneously uses the echo signals to localize multiple point targets in the three-dimension (3D) space. To balance the performance tradeoff between near-field communication and 3D target localization, we design the transmit covariance matrix at the BS to optimize the localization performance while ensuring the signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs. In particular, we formulate three design problems by considering different 3D localization performance metrics, including minimizing the sum Cramér-Rao bound (CRB) for estimating 3D locations, maximizing the minimum target illumination power, and maximizing the minimum target echo signal power. Although the three design problems are non-convex in general, we obtain their global optimal solutions via the technique of semi-definite relaxation (SDR) by proving the tightness of such relaxations. It is rigorously shown that the optimal solutions to the three problems have low-rank structures depending on the sensing and communication channel matrices, which can be exploited to greatly reduce the computational complexity of the SDR-based solutions. Interestingly, we find that in the special case with a single collocated target/CU present towards the middle of a symmetric uniform planar array (UPA), the optimal solutions to the three problems become identical to the SINR-maximization design and have a closed form, while in other cases they can be different in general. Besides, when the target/CU moves away from the transmitter/receiver, the CRB may first decrease and then increase. These two phenomena differ from those in the far-field scenario. Numerical results show the benefits of the proposed near-field designs in optimizing both sensing and communication performance, by exploiting the beam focusing capabilities of ELAA, while the benchmark based on far-field design yields inferior results due to model mismatch.

This paper unifies integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT), by investigating a new multi-functional multiple-input multiple-output (MIMO) system that integrates wireless sensing, communication, and powering. In this system, a multi-antenna hybrid access point (H-AP) transmits wireless signals to communicate with a multi-antenna information decoding (ID) receiver, wirelessly charges a multi-antenna energy harvesting (EH) receiver, and performs radar target sensing based on the echo signal concurrently. Under this setup, we aim to reveal the fundamental performance tradeoff limits among sensing, communication, and powering, in terms of the estimation Cramér-Rao bound (CRB), achievable communication rate, and harvested energy, respectively. In particular, we consider two different target models for radar sensing, namely the point and extended targets, for which we are interested in estimating the target angle and the complete target response matrix, respectively. For both models, we define the achievable CRB-rate-energy (C-R-E) region and characterize its Pareto boundary by maximizing the achievable rate at the ID receiver, subject to the estimation CRB requirement for target sensing, the minimum harvested energy requirement at the EH receiver, and the maximum transmit power constraint at the H-AP. We obtain partitionable optimal transmit covariance matrix solutions to the two formulated problems by applying advanced convex optimization techniques. The numerical results demonstrate the optimal C-R-E region boundary achieved by our proposed design, as compared to the benchmark schemes based on time division and eigenmode transmission (EMT).

This paper studies a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) sends unified wireless signals to estimate one sensing target and communicate with a multi-antenna communication user (CU) simultaneously. We consider two sensing target models, namely the point and extended targets, respectively. For the point target case, the BS estimates the target angle and the reflection coefficient as unknown parameters, and we adopt the Cramér-Rao bound (CRB) for angle estimation as the sensing performance metric. For the extended target case, the BS estimates the complete target response matrix, and we consider three different sensing performance metrics including the trace, the maximum eigenvalue, and the determinant of the CRB matrix for target response matrix estimation. For each of the four scenarios with different CRB measures, we investigate the fundamental tradeoff between the estimation CRB for sensing and the data rate for communication, by characterizing the Pareto boundary of the achievable CRB-rate (C-R) region. In particular, we formulate a new MIMO rate maximization problem for each scenario, by optimizing the transmit covariance matrix at the BS, subject to a different form of maximum CRB constraint and its maximum transmit power constraint. For these problems, we obtain the optimal transmit covariance solutions in semi-closed forms by using advanced convex optimization techniques. For the point target case, the optimal solution is obtained by diagonalizing acomposite channel matrixvia singular value decomposition (SVD) together with water-filling-like power allocation over these decomposed subchannels. For the three scenarios in the extended target case, the optimal solutions are obtained by diagonalizing thecommunication channelvia SVD, together with proper power allocation over two orthogonal sets of subchannels, one for both communication and sensing, and the other for dedicated sensing only. Finally, numerical results show the C-R region achieved by the optimal design in each scenario, which significantly outperforms that by other benchmark schemes such as time switching.

Future sixth-generation (6G) networks are envisioned to provide both sensing and communications functionalities by using densely deployed base stations (BSs) with massive antennas operating in millimeter wave (mmWave) and terahertz (THz). Due to the large number of antennas and the high frequency band, the sensing and communications are expected to be implemented within the near-field region, thus making the conventional designs based on the far-field channel models inapplicable. This paper studies a near-field multiple-input-multiple-output (MIMO) radar sensing system, in which the transceivers with massive antennas aim to localize multiple near-field targets in the three-dimensional (3D) space. In particular, we adopt a general wavefront propagation model by considering the exact spherical wavefront with both channel phase and amplitude variations over different antennas. Besides, we consider the general transmit signal waveforms and also consider the unknown cluttered environments. Under this setup, the unknown parameters to estimate include the 3D coordinates and the complex reflection coefficients of the targets, as well as the noise and interference covariance matrix. Accordingly, we derive the Fisher information matrix (FIM) corresponding to the 3D coordinates and the complex reflection coefficients of the targets and accordingly obtain the Cramér-Rao bound (CRB) for the 3D coordinates. This provides a performance bound for 3D near-field target localization. Next, to facilitate practical localization, we propose an efficient estimation algorithm based on the 3D approximate cyclic optimization (3D-ACO), which is obtained following the maximum likelihood (ML) criterion. Finally, numerical results show that considering the exact antenna-varying channel amplitudes achieves more accurate CRB as compared to prior works based on constant channel amplitudes across antennas, especially when the targets are close to the transceivers. It is also shown that the proposed estimator achieves localization performance close to the derived CRB, thus validating its effectiveness in practical implementation.

This paper unifies integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT), by investigating a new multi-functional multiple-input multiple-output (MIMO) system integrating wireless sensing, communication, and powering. In this system, one multi-antenna hybrid access point (H-AP) transmits wireless signals to communicate with one multi-antenna information decoding (ID) receiver, wirelessly charge one multi-antenna energy harvesting (EH) receiver, and perform radar sensing for a point target based on the echo signal at the same time. Under this setup, we aim to reveal the fundamental performance tradeoff limits of sensing, communication, and powering, in terms of the estimation Cramér-Rao bound (CRB), achievable communication rate, and harvested energy level, respectively. Towards this end, we define the achievable CRB-rate-energy (C-R-E) region and characterize its Pareto boundary by maximizing the achievable rate at the ID receiver, subject to the estimation CRB requirement for target sensing, the harvested energy requirement at the EH receiver, and the maximum transmit power constraint at the H-AP. We obtain the semi-closed-form optimal transmit covariance solution to the formulated problem by applying advanced convex optimization techniques. Numerical results show the optimal C-R-E region boundary achieved by our proposed design, as compared to the benchmark scheme based on time switching.

This paper studies a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) sends unified wireless signals to estimate an extended target and communicate with a multi-antenna communication user (CU) at the same time. We investigate the fundamental tradeoff between the estimation Cramér-Rao bound (CRB) for sensing and the data rate for communication, by characterizing the Pareto boundary of the achievable CRB-rate (C-R) region. Towards this end, we formulate a new MIMO rate maximization problem by optimizing the transmit covariance matrix at the BS, subject to a new form of maximum CRB constraint together with a maximum transmit power constraint. We derive the optimal transmit covariance solution in a semi-closed form, by first implementing the singular-value decomposition (SVD) to diagonalize the communication channel and then properly allocating the transmit power over these subchannels for communication and other orthogonal subchannels (if any) for dedicated sensing. It is shown that the optimal transmit covariance is of full rank, which unifies the conventional rate maximization design with water-filling power allocation and the CRB minimization design with isotropic transmission. Numerical results are provided to validate the performance achieved by our proposed optimal design, in comparison with other benchmark schemes.

This paper studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS with a uniform linear array (ULA) is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing potential targets at areas with LoS links blocked. We consider that the BS transmits combined information and sensing signals for ISAC. Under this setup, we jointly optimize the transmit information and sensing beamforming at the BS and the reflective beamforming at the IRS, to maximize the IRS’s minimum beampattern gain towards the desired sensing angles, subject to the minimum signal-to-noise ratio (SNR) requirement at the CU and the maximum transmit power constraint at the BS. Although the formulated SNR-constrained beampattern gain maximization problem is non-convex and difficult to solve, we present an efficient algorithm to obtain a high-quality solution by using the techniques of alternating optimization and semi-definite relaxation (SDR). Numerical results show that the proposed joint beamforming design achieves improved sensing performance while ensuring the communication requirement as compared to benchmarks without such joint optimization. It is also shown that the use of dedicated sensing beams is beneficial in enhancing the performance for IRS-assisted ISAC.

This paper studies the transmit beamforming in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) equipped with a uniform linear array (ULA) sends combined information-bearing and dedicated radar signals to simultaneously perform downlink multiuser communication and radar target sensing. Under this setup, we minimize the radar sensing beampattern matching errors, subject to the communication users' minimum signal-to-interference-plus-noise ratio (SINR) requirements and the BS's transmit power constraints. In particular, we consider two types of communication receivers, namely Type-I and Type-II receivers, which do not have and do have the capability of cancelling the interference from the a-priori known dedicated radar signals, respectively. Under both Type-I and Type-II receivers, the nonconvex beampattern matching problems are globally optimally solved via applying the semidefinite relaxation (SDR) technique. It is shown that at the optimality, dedicated radar signals are not required with Type-I receivers under some specific conditions, while dedicated radar signals are always needed to enhance the performance with Type-II receivers. Numerical results show that by exploiting the capability of canceling the interference caused by the radar signals, the case with Type-II receivers results in better sensing performance in terms of beampattern matching error than that with Type-I receivers and other conventional designs.

This paper presents an MRAM-based deep in-memory architecture (MRAM-DIMA) to efficiently implement multi-bit matrix vector multiplication for deep neural networks using a standard MRAM bitcell array. The MRAM-DIMA achieves an 4.5 × and 70× lower energy and delay, respectively, compared to a conventional digital MRAM architecture. Behavioral models are developed to estimate the impact of circuit non-idealities, including process variations, on the DNN accuracy. An accuracy drop of ≤ 0.5% (≤ 1%) is observed for LeNet-300-100 on the MNIST dataset (a 9-layer CNN on the CIFAR-10 dataset), while tolerating 24% (12%) variation in cell conductance in a commercial 22 nm CMOS-MRAM process.