This paper proposes an approach that leverages multimodal data by integrating visual images with radio frequency (RF) pilots to optimize user association and beamforming in a downlink wireless cellular network under a max-min fairness criterion. Traditional methods typically optimize wireless system parameters based on channel state information (CSI). However, obtaining accurate CSI requires extensive pilot transmissions, which lead to increased overhead and latency. Moreover, the optimization of user association and beamforming is a discrete and non-convex optimization problem, which is challenging to solve analytically. In this paper, we propose to incorporate visual camera data in addition to the RF pilots to perform the joint optimization of user association and beamforming. The visual image data help enhance channel awareness, thereby reducing the dependency on extensive pilot transmissions for system optimization. We employ a learning-based approach based on using first a detection neural network that estimates user locations from images, and subsequently two graph neural networks (GNNs) that extract features for system optimization based on the location information and the received pilots, respectively. Then, a multimodal GNN is constructed to integrate the features for the joint optimization user association and beamforming. Simulation results demonstrate that the proposed method achieves superior performance, while having low computational complexity and being interpretable and generalizable, making it an effective solution as compared to traditional methods based only on RF pilots.

This paper studies an uplink dual-functional sensing and communication system aided by a reconfigurable intelligent surface (RIS), whose reflection pattern is optimally configured to trade-off sensing and communication functionalities. Specifically, the Bayesian Cramér-Rao lower bound (BCRLB) for estimating the azimuth angle of a sensing user is minimized while ensuring the signal-to-interference-plus-noise ratio constraints for communication users. We show that this problem can be formulated as a novel fractionally constrained fractional programming (FCFP) problem. To deal with this highly nontrivial problem, we extend a quadratic transform technique, originally proposed to handle optimization problems containing fractional structures only in objectives, to the scenario where the constraints also include ratios. First, we consider the case where the fading coefficient is known. Using the quadratic transform, the FCFP problem can be turned into a sequence of subproblems that are convex except for the constant-modulus constraints which can be tackled using a penalty-based approach. To further reduce the computational complexity, we leverage the constant-modulus conditions and propose a novel linear transform. This new transform enables the FCFP problem to be turned into a sequence of linear programming (LP) subproblems, which can be solved with linear complexity in the dimension of reflecting elements. Then, we consider the case where the fading coefficient is unknown. A modified BCRLB is used to make the problem more tractable, and the proposed quadratic transform-based algorithm is used to solve the problem. Numerical results unveil nontrivial and effective reflection patterns that can be synthesized by the RIS to facilitate both communication and sensing functionalities.

Adopting low-resolution hardware at transceivers in multi-input multi-output (MIMO) sensing systems can substantially reduce hardware costs and power consumption. This motivates us to study MIMO sensing systems with hardware constraints, specifically phase-only analog transmit antennas and low-resolution receive antennas. This paper adopts a Bayesian approach and aims to design low-complexity algorithms for the MIMO sensing beamforming problem while leveraging prior information about the target at each sensing stage. We formulate the problem of minimizing the Bayesian Cramér-Rao lower bound (BCRLB) for estimating a parameter of interest, and show that it has the structure of a weighted sum-of-ratios problem. For the case where the phase shifters at transmit antennas are continuous, we propose a novel linear transform that can transform a fractional function into a linear function. In this way, the original problem is turned into a sequence of sub-problems that can be solved in closed-form in each step with linear complexity in the number of antennas, making the iterative optimization process highly efficient. When the phase shifters are discrete, we propose a penalty-based convex-hull relaxation algorithm, which provides better performance than directly quantizing the solution of the continuous case, but at the cost of increased computational complexity. Numerical results demonstrate the effectiveness of the proposed algorithms.

This paper studies an uplink dual-functional sensing and communication system assisted by an active or passive reconfigurable intelligent surface (RIS), whose reflection pattern is optimally configured to trade off sensing and communication functionalities. Specifically, the Bayesian Cramér-Rao lower bound (BCRLB) for sensing is minimized under the quality-of-service (QoS) communication constraints. We show that this problem can be formulated as a fractionally constrained fractional programming (FCFP) problem for which a quadratic transform, originally proposed for the sum-of-ratio fractional programs, can be used to decouple the numerators and denominators in both the objective function and the constraints. In this way, the FCFP is turned into a sequence of sub-problems that are convex except for the constant-modulus amplitude constraints which can be dealt with using a penalty-based method. Numerical results unveil nontrivial beamforming reflection patterns that the RIS can be configured to generate in order to facilitate both sensing and communications. The results demonstrate the effectiveness of the proposed algorithm.

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold.

The advantages of millimeter-wave and large antenna arrays technologies for accurate wireless localization have received extensive attentions recently. However, how to further improve the accuracy of wireless localization, even in the case with obstructed line-of-sight, is largely undiscovered. In this paper, the reconfigurable intelligent surface (RIS) is introduced into the system to make the positioning more accurate. First, we establish the three-dimensional RIS-assisted wireless localization channel model. After that, we derive the Fisher information matrix and the Cramér-Rao lower bound for evaluating the estimation of absolute mobile station position. Finally, we propose an alternative optimization method and a gradient decent method to optimize the reflect beamforming, which aims to minimize the Cramér-Rao lower bound to obtain a more accurate estimation. Our results show that the proposed methods significantly improve the accuracy of positioning, and decimeter-level or even centimeter-level positioning can be achieved by utilizing the RIS with a large number of reflecting elements.

Reconfigurable intelligent surface (RIS) technology can significantly improve the energy and spectrum efficiency of wireless communication systems. Most existing studies were conducted with an assumption of ideal hardware, while the impact of hardware impairments receives little attention. However, the non-negligible hardware impairments should be taken into consideration when we evaluate the system performance. In this paper, we consider an RIS assisted communication system with hardware impairments, and focus on the channel estimation study and the power scaling law analysis. First, with linear minimum mean square error estimation, we theoretically characterize the relationship between channel estimation performance and impairment level, number of reflecting elements, and pilot power. After that, we analyze the power scaling law and reveal that if the base station (BS) has perfect channel state information, the transmit power of user can be made inversely proportional to the BS antenna number and the square of the reflecting element number with no reduction in performance; If the BS has imperfectly estimated channel state information, to achieve the same performance, the transmit power of user can be made inversely proportional to the square-root of the BS antenna number and the square of the reflecting element number.

Intelligent reflecting surface (IRS) technology has emerged as a promising way to improve the energy efficiency of wireless communication systems with less complexity and hardware cost. Most existing studies are conducted with ideal hardware, however, both physical transceiver and IRS suffer from non-negligible hardware impairments which may greatly degrade the system performance. In this paper, by considering hardware impairments, we focus on the energy efficiency analysis of IRS system. Our first contribution is to derive the optimal receive combining and transmit beamforming vectors. After that, we characterize the asymptotic channel capacity. With the derived asymptotic channel capacity and the power consumption model, the analytical upper and lower bounds on the maximal energy efficiency are provided. Our results show that an IRS system can achieve both high spectral efficiency and high energy efficiency with moderate number of antennas. This observation is encouraging for that there is no need to cost a lot on expensive high-quality antennas, which corresponds to the requirements of new communication paradigms.