VTC 2025 Spring Conference’s Shorts

Multi-Agent Deep Reinforceinent Learning-Based Offloading Computation and Routing in Coope...

The increasing demand for tasks and dynamically changing loads in the Low Earth Orbit (LEO) satellite networks creates significant challenges in terms of computing and routing. Currently, LEO satellites primarily offload tasks to ground stations or satellites within their line of sight, failing to fully utilize the computational resources of the entire network. In addition, existing routing algorithms fail to consider on-satellite loads and computational capacities, leading to bottlenecks in network routing as some satellites with limited processing capacity become overwhelmed. In this paper, the tasks generated by the source satellite can be offloaded to either satellites or ground stations while routing to the destination satellite. The offloading computation and routing decision problems are investigated to minimize the maximum delay. To solve this challenging problem, we first convert the optimization variables, encompassing both routing and computation offloading, into a form that depends solely on the latter, and model the problem as the Markov Decision Process (MDP). Subsequently, the problem is addressed using an algorithm based on Multi-Agent Proximal Policy Optimization (MAPPO), where multiple agents cooperatively determine routing and offloading computation strategies. Simulation results show that the proposed scheme achieves better delay performance.Multi-Agent Deep Reinforceinent Learning-Based Offloading Computation and Routing in Cooperative LEO Satellite Communication NetworkYunyi Yan, Ming Zeng, Zijian Yang, Zesong Fei, Beijing Institute of Technology

Latency and Memory Constraints Aware Protocol to Optimize Data Throughput at a 5G-NR Base-...

This paper analyzes the memory and latency requirements at a SG-NR base station that supports dual-SIM dual-radio cell phones that periodically tune away to monitor voice-call pages on a 3G network. Data transfer to the phone is modeled using a stop-and-wait protocol. We derive expressions for the mean of the number of attempts and the mean of the square of attempts taken to transmit the data. Data arrival at the base-station is modeled as a Poisson process, and the Average Number in Queue and the Average Wait Time are derived using the Pollaczek-Khintchine formula for an M/G/1 queue. It is shown that both are higher for a given arrival rate when there is a complete tune-away versus the tune-away of a single antenna. Consequently, we propose a protocol that requires a dual-SIM phone to request a lower rank to tune away only the diversity antenna. Further, to operate within the memory and latency corresponding to the original single-SIM budgets, we propose a protocol between the base station and the data source to reduce the data-rate. An algorithm is proposed to determine the data-rate based on the tune-away duration and the original budgets.Latency and Memory Constraints Aware Protocol to Optimize Data Throughput at a 5G-NR Base-Station Supporting Multi-SIM PhonesN R Sanjeev, Indian Institute of Technology Hyderabad; Abhinav Kumar, Indian Institute of Technology Hyderabad (IITH)

A Multi-source DOA Estimation Algorithm for UAV Coordinate Systems

Multi-source direction of arrival (DOA) estimation based on mobile platforms draws much attention for its extensive practical applications, among which that unmanned aerial vehicle (UAV) platform is particularly valuable. To ensure the precision and spatial resolution, multi-source DOA estimation always relies on large-scaled antenna arrays. With the rapid development of UAV and its coordinate technology, high-accuracy multi-source DOA estimation based on UAV coordinate system becomes possible. However, due to all kinds of external disturbances, multi-source DOA estimation for the UAV coordinate system suffers the random UAV unit positions and pointing directions, and there is always unknown phase error between adjacent units. To handle these problems, a Fourth-order Cumulant (FoC) based DOA Matrix algorithm is proposed. A DOA Matrix is constructed by FoCs using the received signals, and its eigenvectors are used to acquire the manifold of the entire array, which can be used to solve the implied DOAs. The simulation results verify the availability of the proposed algorithm in the UAV coordinate system, while the traditional DOA estimation methods are disabled. Meanwhile, it gets at least the similar estimation performance as the traditional methods in their own ideal conditions.A Multi-source DOA Estimation Algorithm for UAV Coordinate SystemsChenhao Zhang, Wenjie Wang, Xi Hong, Xi'an Jiaotong University

Ray Antenna Array: A Novel Cost-Effective Multi-Antenna Architecture for Enhanced Wireless...

This paper proposes a novel multi-antenna architecture, termed ray antenna array (RAA), which aims to enhance wireless communication performance in a cost-effective manner. RAA is composed of massive cheap antenna elements and a few radio frequency (RF) chains. The massive antenna elements are arranged in a novel ray-like structure, with each ray corresponding to a simple uniform linear array (sULA) with a carefully designed orientation. The antenna elements of each sULA are directly connected to an RF combiner, so that the sULA in each ray is able to form a beam towards a direction matching the ray orientation without relying on any analog or digital beamforming. By further designing a ray selection network (RSN), appropriate sULAs are selected to connect to the RF chains for further baseband processing. Compared to conventional multi-antenna architectures like hybrid analog/digital beamforming (HBF), the proposed RAA has two major advantages. First, it can significantly reduce hardware cost since no phase shifters, which are usually expensive especially in high-frequency systems, are required. Besides, RAA can greatly improve system performance by configuring antenna elements with higher directionality, as each sULA only needs to be responsible for a portion of the total coverage angle. To demonstrate such advantages, in this paper, we first present the input-output model for RAA-based wireless communications, based on which the ray orientations of the RAA are designed. Furthermore, efficient algorithms for joint ray selection and beamforming are proposed for single-user and multi-user RAA-based wireless communications. Simulation results demonstrate the superior performance of RAA compared to HBF while significantly reducing hardware cost.Ray Antenna Array: A Novel Cost-Effective Multi-Antenna Architecture for Enhanced Wireless CommunicationZhenjun Dong, Zhiwen Zhou, Yong Zeng, Southeast University

Phased array with movable antennas and beampattern synthesis

In this paper, we propose a novel phased array with movable antennas (PAMA). By replacing traditional phase shifters with movable antennas, PAMA eliminates insertion loss of phase shifters and enhances antenna performance by leveraging the increased degrees of freedom in antenna positioning. We construct a periodic approximation-based beampattern synthesis (PABS) method. It leverages the approximate periodicity of the PAMA array response to obtain a high-quality initial solution through discrete optimization with quadruple-frequency sampling, which is subsequently refined using projected gradient descent. Simulations demonstrate the effectiveness of the proposed approach for multi-beampattern synthesis and highlight the impact of antenna movement range on synthesis performance. The results show that PAMA-based beampattern synthesis enhances beamforming accuracy and improves efficiency, especially in practical scenarios where digital phase shifters are used.Phased array with movable antennas and beampattern synthesisKewei Zhu, Haifan Yin, Huazhong University of Science and Technology; Deepak Mishra, University of New South Wales (UNSW) Sydney; Jinhong Yuan, University of New South Wales

Moving port prediction: Converting time-varying to static channels with fluid antennas

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction method, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. In the performance analysis, we derive the asymptotical lower and upper bounds of the prediction error for a multipath channel, when the number of base station (BS) antennas and the port density of the FA are large enough. When the UEs move at a speed of 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction method.Moving port prediction: Converting time-varying to static channels with fluid antennasWeidong Li, Haifan Yin, Fanpo Fu, Yandi Cao, Huazhong University of Science and Technology; M\'{e}rouane Debbah, KU 6G Research Center, Khalifa University of Science and Technology

Adaptive Multi-Beamforming for Integrated Sensing and Communication System

Multi-beamforming is an effective technique to mitigate interference in the integrated sensing and communication (ISAC) system. This paper proposes an adaptive multi-beamforming approach, in which a demand factor that optimizes the phase weighting factor is developed for synthesizing sensing and communication beams. Based on this demand factor, a novel osprey sparrow optimization (OSO) algorithm is proposed to efficiently calculate the optimal sensing and communication beamforming vectors. Simulation results demonstrate that the proposed approach achieves the desired multi-beamforming, catering to the requirements of sensing and communication. OSO suppresses side lobes with a reduction of 5-10 dB and enjoys more accurate main lobe pointing for linear antenna arrays (LAAs), compared to either sparrow search algorithm (SSA) or particle swarm optimization (PSO). When the size of antenna arrays becomes larger, OSO achieves at least 3-fold convergence speed improvement for planar antenna arrays (PAAs).Adaptive Multi-Beamforming for Integrated Sensing and Communication SystemJieming Xie, Haoyu Lu, Hongcheng Zhuang, Lin Zhang, Sun Yat-sen University

A Novel Robust Adaptive Beamforming Method for Null Broadening and Interference Mitigation...

In high-dynamic environments, such as satellites, drones, and autonomous vehicles, the performance of traditional adaptive beamforming methods can be significantly affected by rapidly moving interference. Such interference may shift out of nulls and sometimes enter the main lobe—an issue that most existing methods struggle to handle. To overcome the challenge of static null broadening and main lobe invasion, a new adaptive beamforming algorithm is proposed through two synergistic innovations: real-time null broadening and dynamic main lobe interference suppression. By adding a time attenuation factor in the covariance matrix reconstruction and using virtual interference rotation, the algorithm achieves real-time null broadening and adapts to suppress interference effectively. Additionally, the moving-MUSIC algorithm is used to detect the main lobe interference and estimate its direction, followed by projection elimination to remove it. The simulation results show that the proposed method effectively maintains performance. In conclusion, the proposed method offers a reliable solution for interference suppression in dynamic environments, enhancing the robustness and performance of adaptive beamforming systems.A Novel Robust Adaptive Beamforming Method for Null Broadening and Interference Mitigation in High-Dynamic ScenariosKaichao Zheng, Yuan Jiang, Lei Zhao, Sun Yat-sen University

Two-Timescale Optimization Approach for Caching, Computing, and Communication in MIMO-OFDM...

This paper explores the joint optimization of caching, computing, and communication (3C) in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless networks. It also incorporates the use of reconfigurable intelligent surfaces (RISs) to enhance communication performance. Given the different timescales for updating various resource allocations, we propose a two-timescale joint 3C optimization approach aimed at minimizing network latency. In this approach, task offloading, computing power allocation, precoding, and RIS optimization occur on the small timescale, while caching is updated on the large timescale. Our simulation results demonstrate that this approach effectively reduces network latency and outperforms the reference schemes.Two-Timescale Optimization Approach for Caching, Computing, and Communication in MIMO-OFDM Wireless Networks With and Without RISsShin-Ping Huang, Ming-Chun Lee, Ming-Hsiang Ku, National Yang Ming Chiao Tung University

Deep Learning-Based Location to Precoding Mapping in Massive MIMO Systems

Channel acquisition for precoding design in massive multiple-input multiple-output (MIMO) systems faces increasingly prominent challenges due to the huge overhead caused by channel feedback and transmission of pilot signals. In response, directly mapping location information to precoding without channel feedback has emerged as a feasible and efficient solution. However, existing deep learning-based methods often struggle to map low-dimensional positional data to high-dimensional precoding vectors accurately. To address this challenge, we propose a spatially adaptive mapping network (SAM-Net) that enhances feature extraction and representation by leveraging transposed convolution and incorporating spatial information for fine-grained adaptive adjustments. While SAM-Net improves mapping performance, its complexity also increases. Therefore, we introduce a lightweight spatially adaptive mapping network (LSAM-Net) that combines average pooling and max pooling to reduce the number of parameters and computational complexity while it maintains near-optimal performance. Evaluation results demonstrate that SAM-Net achieves superior and stable mapping performance, while LSAM-Net offers a more lightweight alternative with minimal loss in performance.Deep Learning-Based Location to Precoding Mapping in Massive MIMO SystemsFen He, Haozhen Li, Xinyu Gu, Beijing University of Posts and Telecommunications; Zhenyu Liu, University of Surrey; Liyang Lu, State Key Lab of Intelligent Transportation System