Publisher Copyright: © 2026 The Authors.

Robust hybrid beamforming for integrated sensing and communications (ISAC) system under bounded uncertainties in sensing reception is developed using algorithm unrolling technique. First, the robust hybrid beamforming design problem is formulated as an optimization problem that jointly maximizes the communication sum-rate and the worst-case sensing mutual information under the uncertainty of receive steering vector. Then, a benchmark method using projected gradient descent and ascent (PGDA) algorithm is designed to solve this optimization problem. Finally, we propose to unroll the developed PGDA algorithm using the algorithm unrolling technique. Numerical results demonstrate the advantages of the unrolled PGDA algorithm over the PGDA benchmark for addressing the newly introduced problem of robust hybrid beamforming design for ISAC.

The fast moving characteristics of high-speed trains pose a challenge to the beam alignment of high-speed railway millimeter wave communication systems. With powerful learning capabilities, machine learning-based methods can help improve the beam alignment performance, such as greatly reducing the delay. In this paper, a non-convex optimization problem is formulated aiming at maximizing the received power of downlink transmission, and deep reinforcement learning is used to assist beam alignment. Particularly, an adaptive beam alignment algorithm based on prioritized experience replay double deep Q-Learning is proposed to adjust beam direction dynamically. The algorithm observes the position of the train and the beam direction of the train roof mobile relay to guide the beam adjustment. To reduce the adjustment frequency and the requirements for train position accuracy, the service range of remote radio head is divided into multiple location bins. The beam direction adjustment is only executed when the train enters the next location bin. Simulation results verify that compared with other baseline schemes, the proposed algorithm can effectively improve the received power and reduce the beam alignment delay.

Deploying small cell base stations (SBS) under the coverage area of a macro base station (MBS), and caching popular contents at the SBSs in advance, are effective means to provide high-speed and low-latency services in next generation mobile communication networks. In this paper, we investigate the problem of content caching (CC) and user association (UA) for edge computing. A joint CC and UA optimization problem is formulated to minimize the content download latency. We prove that the joint CC and UA optimization problem is NP-hard. Then, we propose a CC and UA algorithm (JCC-UA) to reduce the content download latency. JCC-UA includes a smart content caching policy (SCCP) and dynamic user association (DUA). SCCP utilizes the exponential smoothing method to predict content popularity and cache contents according to prediction results. DUA includes a rapid association (RA) method and a delayed association (DA) method. Simulation results demonstrate that the proposed JCC-UA algorithm can effectively reduce the latency of user content downloading and improve the hit rates of contents cached at the BSs as compared to several baseline schemes.

Wireless Internet-of-Things (IoT) networks empowered by blockchain have became a promising architecture to establish trust and consensus mechanisms in a distributed manner. However, the computational complexity and limited on-board energy of wireless devices impose great challenges on applying blockchain into IoT networks. To address this issue, this work introduces multiple mobile edge computing (MEC) to provide sufficient computational power for miners (i.e., IoT devices). As such, the computation-intensive tasks of the miners can be either computed locally or offloaded to some certain MEC servers to fully exploit the computation resources. Moreover, to decrease the energy consumption of the IoT networks, an optimization problem is formulated to maximize the energy efficiency of IoT devices by jointly optimizing the computation mode selection and power allocation. Since the formulated problem is generally intractable with mixed-integer variables, an Fmincon-based algorithm is proposed, which guarantees a globally optimal solution. To further reduce the computational complexity of the proposed optimal method, a Deep Neural Network (DNN)-based deep learning method is applied to facilitate the computation of the proposed algorithm. Finally, numerical results demonstrate the advantages of the proposed network architecture and the algorithm in terms of both the energy and computational efficiency.