Integrated sensing and communication (ISAC) unifies sensing and communication, and improves the efficiency of the spectrum, energy, and hardware. In this work, we investigate the ISAC beamforming design to maximize the mutual information between the target response matrix of a point radar target and the echo signals, while ensuring the data rate requirements of the communication users. The scenarios in this paper are considered from two perspectives: one is from the perspective of the number of communication users, and the other is from the perspective of the type of echo interference caused by other interfering scatterers. For the case of a single communication user, in order to reduce the complexity of the algorithm, we consider three types of echo interference, no interference, a point interference, and an extended interference. For the case of multiple communication users, the interference is also an extended one, and furthermore, each user's communication rate requirement needs to be satisfied. To find the optimal beamforming design in these problems, we provide a closed-form solution with low complexity, a semidefinite relaxation (SDR) method, a low-complexity algorithm based on the Majorization-Minimization (MM) method and the successive convex approximation (SCA) method, and an algorithm based on MM method and SCA method, respectively. Numerical results demonstrate that, compared to the ISAC beamforming schemes based on the Cramér-Rao bound (CRB) metric, the beampattern metric and the MSE metric, the proposed mutual information metric can bring better beampattern and root mean square error (RMSE) of angle estimation. Furthermore, our proposed schemes designed based on the mutual information metric can suppress the echo interference from interfering scatterers in the environment effectively.

This paper investigates active intelligent reconfigurable surface (IRS)-aided MIMO integrated sensing and communication (ISAC) systems. Different from passive IRS, an active IRS with amplifiers is deployed to overcome the product attenuation caused by the reflection of the IRS. We aim to maximize IRS beampattern towards the direction of a point sensing target, while guaranteeing the quality of service (QoS) requirement of the communication user. The target is sensed by the reflected sensing link, and the user can receive communication signals from both the direct link and the reflected link. To solve the formulated problem with coupled variables, an alternating optimization algorithm is proposed. Approximations such as the majorization-minimization (MM) method is used to tackle the BS beamforming subproblem, while some matrix transformations, the semidefinite relaxation and the Gaussian randomization method is applied to solve the reflection coefficient subproblem. Numerical results demonstrate that active IRS can greatly improve the sensing performance compared to passive IRS.

In this paper, we investigate the intelligent reflecting surface (IRS)/reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system based on sensing mutual information (MI). Specifically, the base station (BS) perceives the sensing target via the reflected sensing signal by the IRS, while communicating with the users simultaneously. Our aim is to maximize the sensing MI, subject to the quality of service (QoS) constraints for all communication users, the transmit power constraint at the BS, and the unit-modulus constraint on the IRS’s passive reflection. We solve this problem under two cases: one simplified case assuming a line-of-sight (LoS) channel between the BS and IRS and no clutter interference to sensing, and the other generalized case considering the Rician fading channel of the BS-IRS link and the presence of clutter interference to sensing. For the first case, we prove that the dedicated sensing beamformer is unnecessary for improving sensing MI and develop a low-complexity iterative algorithm to jointly optimize the BS and IRS active/passive beamformers. Then, for the second case, we propose an alternative iterative algorithm, which can also be applied to the first case, to solve the beamforming design problem under the general setup. Numerical results are provided to validate the performance of the proposed algorithms, as compared to various benchmark schemes.

Reconfigurable Intelligent Surface (RIS) is applied to physical layer security to enhance the security of task offloading in mobile edge computing (MEC) systems. However, due to the “double fading” effect on the reflection link, the deployment of passive RIS can only achieve limited performance gains. In view of these, active RIS is regarded as a prospective candidate for compensating for the “double fading” effect of passive RIS due to its low-power integrated amplifiers. This paper proposes a novel secure MEC system, which improves the communication and computation performance of the system by introducing active RIS. Specifically, we aim to minimize the weighted total energy consumption (TEC) in an active RIS-assisted MEC system, subject to the constraints of secure offloading and the phase shift of the active RIS. To address this non-convex problem, block coordinate descent (BCD) and successive convex approximation (SCA) methods are employed to alternately optimize the subproblems. Under the premise of ensuring the secure transmission rate for users, the computation offloading volume, the edge computing resource allocation, beamforming at the receiver, and reflection coefficient matrix at the active RIS are jointly optimized. Simulation results demonstrate that the active RIS significantly enhances the weighted TEC performance in MEC systems while ensuring secure transmission rates, effectively mitigating the impact of the “double fading” effect, and showing notable advantages over existing passive RIS and non-RIS schemes.

This letter investigates the movable antenna (MA) array enhanced wireless sensing via flexible array geometry reconfiguration at both the transmitter and receiver, where the weighted sum of Cramér-Rao bounds (WSCRB) with respect to elevation and azimuth angles is adopted to evaluate the sensing performance within a given target region. To this end, an optimization problem is formulated to minimize the WSCRB by jointly optimizing transmit and receive MAs’ positions and the covariance matrix of the transmit signal. To address the problem with coupled variables and intractable objective function, a block coordinate descent (BCD) algorithm with momentum gradient descent and log barrier penalties is proposed. Since the complex objective function prevents closed-form gradient computation, we use PyTorch’s autograd function to automatically compute the gradient. Numerical results demonstrate that the proposed MA scheme achieves lower WSCRB and higher sensing accuracy compared to both sparse and dense arrays employing fixed-position antennas.

Ultra-dense networks (UDNs) is one of the key technologies that can offer large capacities with numerous small base stations (BSs) deployed. However, the BSs consume a lot of energy when they are all turned on. To save energy, small cells with zero or low load should be in sleep mode. In this paper, we propose a small cell wake-up strategy based on the mobile application usage prediction of the users. First, LSTM neural network model is used to predict the users’ application usage in the next interval and the training data is from a real-world anonymous datasets. The small BS will be woken up when it is predicted that one or more users in its coverage area will use high data-rate applications in the next time period. Numerical results show that the LSTM method achieves higher prediction precision and recall compared with the other prediction algorithms. Employing our scheme, we can get about 14% gain in energy consumption compared to the energy efficient system where the small cell is woken up based on the predicted number of users in its coverage area.