VTC 2025 Spring Conference’s Shorts

RIS Optimization Algorithms for Urban Wireless Scenarios in Sionna RT

This paper evaluates the performance of reconfigurable intelligent surface (RIS) optimization algorithms, which utilize channel estimation methods, in ray tracing (RT) simulations within urban digital twin environments. Beyond Sionna’s native capabilities, we implement and benchmark additional RIS optimization algorithms based on channel estimation, enabling an evaluation of RIS strategies under various deployment conditions. Coverage maps for RIS-assisted communication systems are generated through the integration of Sionna’s RT simulations. Moreover, real-world experimentation underscores the necessity of validating algorithms in near-realistic simulation environments, as minor variations in measurement setups can significantly affect performance.RIS Optimization Algorithms for Urban Wireless Scenarios in Sionna RTAhmet Esad Güneşer, Kocaeli University; Berkay Şekeroğlu, Boğaziçi University; Sefa Kayraklık, TÜBİTAK BİLGEM; Erhan Karakoca, TUBITAK BILGEM; İbrahim Hökelek, TÜBİTAK; Sultan Aldirmaz Colak, Kocaeli University; Ali Gorcin, Istanbul Technical University

Measurement-Based Spatial Characteristic Analysis for RIS-Assisted mmWave MIMO Channels

Reconfigurable intelligent surface (RIS) is regarded as one of the promising technologies for the sixth-generation mobile communication systems (6G) due to its ability to reshape the wireless channel and improve the communication transmission rate. In this paper, we investigate the spatial characteristic of an RIS-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) channel through channel measurement in practical environment. The channel measurement campaigns are carried out in an indoor hotspot (InH) non-line-of-sight (NLOS) scenario at 35 GHz. We compare and analyze the channel parameters in three propagation modes, including intelligent reflection with RIS (IRWR), specular reflection with RIS (SRWR), and without RIS (WR). Different types of receiver (RX) antenna are compared, including the horn antenna and the omni-directional antenna. Signals with bandwidths of 300 MHz and 10 MHz are transmitted to compare the spatial characteristics of wideband and narrowband signals. The measured channel frequency response (CFR) reveals that the RIS coding scheme, antenna type, and signal bandwidth are significant factors influencing the spatial correlation of the wireless channel. The measured power azimuth spectrum (PAS), fitted by truncated Laplacian functions, demonstrates that the deployment of RIS with energy-focused phase configuration results in an amplification of the power in the primary signal propagation direction. Moreover, the analysis of the root mean square (RMS) angular spread shows that it increases with the growing distance between the RX and RIS.Measurement-Based Spatial Characteristic Analysis for RIS-Assisted mmWave MIMO ChannelsChenhong Yang, Jian Sang, Boning Gao, Zi’ang Wang, Xiao Li, Wankai Tang, Southeast University; Shi Jin, Southern University; Haiming Wang, Southeast University

Improved AFSA-Based Beam Training Without CSI for RIS-Assisted ISAC Systems

In this paper, we consider transmit beamforming and reflection patterns design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems, where the dual-function base station (DFBS) lacks channel state information (CSI). To address the high overhead of cascaded channel estimation, we propose an improved artificial fish swarm algorithm (AFSA) combined with a feedback-based joint active and passive beam training scheme. In this approach, we consider the interference caused by multipath user echo signals on target detection and propose a beamforming design method that balances both communication and sensing performance. Numerical simulations show that the proposed AFSA outperforms other optimization algorithms, particularly in its robustness against echo interference under different communication signal-to-noise ratio (SNR) constraints.Improved AFSA-Based Beam Training Without CSI for RIS-Assisted ISAC SystemsYunxiang Shi, Lixin Li, Wensheng Lin, Wei Liang, Northwestern Polytechnical University; Zhu Han, University of Houston

Active Element Selection for Channel Estimation in RIS-Aided Wireless Communication System...

The hybrid reconfigurable intelligent surface (RIS) architecture consisting of both active elements (AEs) and passive elements (PEs) is promising for use in millimeter wave wireless communication systems. In this paper, we propose a novel scheme for selection of AEs in this kind of RIS-aided system to enhance the channel estimation performance. The RIS is first partitioned into multiple subregions and an appropriate number of AEs are then selected strategically from each subregion according to some criteria. A greedy coordinate descent algorithm is also proposed to reduce the computational complexity of the selection process. With the selected AEs, channel estimation is performed between the user equipment and RIS based on compressed sensing. Computer simulation results demonstrate that the proposed AE selection scheme induces a significant performance improvement in channel estimation over the random AE selection approach.Active Element Selection for Channel Estimation in RIS-Aided Wireless Communication SystemsChin-Liang Wang, Ying Chen, Min-Hsien Ko, National Tsing Hua University

Resource Allocation in IRSA-Assisted NOMA for Massive URLLC Using Lightweight Q-Learning

Ultra-Reliable Low-Latency Communication is the Fifth Generation (5G) use case with the most stringent requirements for latency and reliability. In Beyond 5G and future 6G systems, there will be a need to support a large number of URLLC devices, giving rise to a new use case known as massive URLLC (mURLLC). Addressing these demands requires efficient resource sharing among multiple devices. Non-Orthogonal Multiple Access (NOMA) emerges as an efficient solution to enhance spectral efficiency by allowing simultaneous transmissions from multiple devices over shared resources. In this paper, we propose a novel joint sub-channel allocation and power control framework that integrates Irregular Repetition Slotted ALOHA (IRSA) with Grant-Free NOMA (GF-NOMA). The resource allocation problem is formulated as a multi-agent reinforcement learning task, where each device acts as a learning agent and the gNodeB (gNB) broadcasts global feedback to meet the stringent reliability and latency requirements. The framework introduces new Quality Scores (QS) that guide agents in selecting resources more efficiently. Extensive simulations demonstrate that the proposed framework significantly outperforms existing techniques in meeting the stringent mURLLC requirements.Resource Allocation in IRSA-Assisted NOMA for Massive URLLC Using Lightweight Q-LearningIbtissem OUESLATI, University of Limoges; Oussama Habachi, LIMOS; Jean-Pierre Cances, ENSIL; Essaid Sabir, Teluq University; Vahid Meghdadi, University of Limoges

Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-air

Over-the-air computation (AirComp) has recently emerged as a pivotal technique for communication-efficient federated learning (FL) in resource-constrained wireless networks. Though AirComp leverages the superposition property of multiple access channels for computation, it inherently limits its ability to manage inter-task interference in multi-task computing. In this paper, we propose a quantized analog beamforming scheme at the receiver to enable simultaneous multi-task FL. Specifically, inspiring by the favorable propagation and channel hardening properties of large-scale antenna arrays, we propose an analog beamforming method in closed form for statistical interference elimination. Analytical results reveal that the interference power vanishes by an order of O (1/Nr) with the number of analog phase shifters, Nr, irrespective of their quantization precision. Numerical results demonstrate the effectiveness of the proposed analog beamforming method and show that the performance upper bound of ideal learning without errors can be achieved by increasing the number of low-precision analog phase shifters.Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-airJiacheng Yao, Wei Xu, Southeast University; Guangxu Zhu, Shenzhen Research Institute of Big Data; Zhaohui Yang, Zhejiang University; Kaibin Huang, The University of Hong Kong; Dusit Niyato, Nanyang Technological University

Provable Performance Bounds for Digital Twin-driven Deep Reinforcement Learning in Wireles...

Digital twin (DT)-driven deep reinforcement learning (DRL) has emerged as a promising paradigm for wireless network optimization, offering safe and efficient training environment for policy exploration. However, in theory existing methods can hardly guarantee real-world performance of DT-trained policies before actual deployment. In this paper, we propose the DT bisimulation metric (DT-BSM), a novel metric based on the Wasserstein distance, to quantify the discrepancy between Markov decision processes (MDPs) in both the DT and the corresponding real-world wireless network environment. We prove that for any DT-trained policy, the sub-optimality of its performance (regret) in the real-world deployment is bounded by a weighted sum of the DT-BSM and its sub-optimality within the MDP in the DT, and a modified DT-BSM based on the total variation distance is introduced to avoid the prohibitive calculation complexity of Wasserstein distance for large-scale wireless network scenarios. Numerical experiments validate this first theoretical finding on the provable and calculable performance bounds for DT-driven DRL.Provable Performance Bounds for Digital Twin-driven Deep Reinforcement Learning in Wireless Networks: A Novel Digital Twin Evaluation MetricZhenyu Tao, Wei Xu, Xiaohu You, Southeast University

Group-based Client Sampling in Multi-Model Federated Learning

Federated learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. In practical scenarios, clients frequently engage in training multiple models concurrently, referred to as multi-model federated learning (MMFL). While concurrent training is generally faster than training one model at a time, MMFL exacerbates traditional FL challenges like the presence of non-i.i.d. data: since each individual client may only be able to train one model in each training round due to local resource limitations, the set of clients training each model will change in each round, introducing instability when clients have different data distributions. Existing single-model FL approaches leverage inherent client clustering to accelerate convergence in the presence of such data heterogeneity. However, since each MMFL model may train on a different dataset, extending these ideas to MMFL requires creating a unified cluster or group structure that supports all models while coordinating their training. In this paper, we present the first group-based client-model allocation scheme in MMFL able to accelerate the training process and improve MMFL performance. We also consider a more realistic scenario in which models and clients can dynamically join the system during training. Empirical studies in real-world datasets show that our MMFL algorithms outperform several baselines up to 15%, particularly in more complex and statically heterogeneous scenarios.Group-based Client Sampling in Multi-Model Federated LearningZejun Gong, Haoran Zhang, Carnegie Mellon University; Marie Siew, Singapore University of Technology and Design; Carlee Joe-Wong, Carnegie Mellon University; Rachid El-Azouzi, University of Avignon

Unified Chirp Sequence Generation for OFDM-based ISAC System with TDM/FDM/CDM Resource Mul...

Integrated sensing and communication (ISAC) is an important new research topic for 5th-generation communication-Advanced (5G-A) and 6th-generation (6G) mobile communication systems, where waveform design should be studied. Orthogonal frequency division multiplexing (OFDM) is anticipated to be more favorable in the future 5G-A and 6G ISAC systems due to its good 5G New Radio (NR) backward compatibility. Different resource multiplexing methods, including time-division multiplexing (TDM), frequency-division multiplexing (FDM), and code-division multiplexing (CDM), for sensing and communication can be assumed in the OFDM-based ISAC system. Due to the low peak-to-average power ratio (PAPR) and good ambiguity function properties, chirp sequence can be used for sensing in the OFDM-based ISAC system. This paper proposes a unified chirp sequence generation method for OFDM-based ISAC system. By parameter configuration, different resource multiplexing methods of sensing and communication, including TDM, FDM, and CDM, can be realized based on the proposed method. Evaluation results demonstrate that the proposed chirp sensing sequence achieves better PAPR performance for both TDM and FDM methods and lower sidelobe level of the ambiguity function for TDM compared to OFDM with Zadoff-Chu (ZC) sequence, which indicates that the proposed chirp sequence with TDM and FDM can be considered as a suitable sensing sequence for scenarios with coverage limitations.Unified Chirp Sequence Generation for OFDM-based ISAC System with TDM/FDM/CDM Resource MultiplexingJuan Liu, DOCOMO Beijing Communication Laboratories Co., Ltd.; Wenjia LIU, DOCOMO Beijing Labs; Xiaolin Hou, DOCOMO Beijing Communications Laboratories Co., Ltd; chen lan, DOCOMO Beijing Communications Lab

Opportunistic Beamforming and Dynamic Scheduling for Multi-User MIMO-ISAC Systems

This research presents a novel framework integrating Flexible-Duplex (FlexD) and Integrated Sensing and Communications (ISAC) technologies to address the challenges of spectrum efficiency and resource optimization in next-generation wireless networks. We develop a unified system model for a dual-functional radar-communication base station with multiple-input multiple-output capabilities, enabling dynamic uplink and downlink channel allocation. The framework maximizes network throughput while maintaining radar sensing performance, subject to signal-to-clutter-plus-noise ratio (SCNR) requirements and power constraints. Given the non-convex and combinatorial nature of the resulting optimization problem, we propose an iterative algorithm that converges to a locally optimal solution. Extensive simulations demonstrate the superiority of the proposed FlexD-ISAC framework compared to conventional half-duplex networks. Additionally, sensitivity analyses reveal the impact of SCNR requirements and power constraints on system performance, providing valuable insights for practical implementation. This work establishes a foundation for future research in dynamic, resource-efficient wireless systems that simultaneously support sensing and communication capabilities.Opportunistic Beamforming and Dynamic Scheduling for Multi-User MIMO-ISAC SystemsTharaka Perera, University of Melbourne; Saman Atapattu, RMIT University; Chathuranga Weeraddana, University of Oulu; Jamie Evans, University of Melbourne