VTC 2025 Spring Conference’s Shorts

Integrated Super-resolution Sensing and Communication with Sparse MIMO for Symbiotic Radio

Integrated super-resolution sensing and communication (IS2 AC) in symbiotic radio (SR) systems enables highly efficient simultaneous localization and information transmission for both primary and secondary user devices. However, accurate estimation of the two-dimensional (2-D) directions of the backscatter devices (BDs) poses a great challenge when they are densely located. To address this issue, in this paper, we propose a sparse multiple-input multiple-output (MIMO)-based IS2 AC SR system with L-shaped nested array (NA) deployed at the base station (BS) to achieve both super-resolution 2-D sensing for multiple secondary BDs and efficient communication for all users. To achieve efficient channel estimation, the elevation and azimuth angles of the primary and secondary users in SR systems are firstly estimated and paired. After that, by matching the paired angles to obtain beamforming gain, the channel gains of both direct and backscatter links are estimated. To evaluate the performance of channel estimation with L-shaped NA, the sum mean square error (MSE) of all channels are analyzed. Simulation results are provided to demonstrate that sparse MIMO can provide significant performance gain than conventional compact MIMO.Integrated Super-resolution Sensing and Communication with Sparse MIMO for Symbiotic RadioJingran Xu, Yong Zeng, Southeast University; Fei Yang, Shanghai Huawei Technologies Co., Ltd.; Yan Chen, Huawei

Impact of Different Antenna Arrangements and Transmitter Tilt to D-Band LoS MIMO Channel

With new technological advancements in wireless communication, reflected in 3GPP (Third Generation Partnership Project) cellular or wireless local area network (WLAN) standards, increasing data rate is a prominent aspect. Consequently, some backhaul networks require a data rate in the order of 100 gigabits per second. The D-Band, which can provide ample bandwidth, potentially can support this data rate. This paper analyzes the line-of-sight D-Band multiple input multiple output (MIMO) channel for different antenna arrangements, which are best suited for achieving the nearly orthogonal channels for exploiting MIMO full-rank spatial multiplexing. This allows for flexibility in antenna arrangement design in hardware, which is not limited to uniform linear or rectangular arrays. We have also addressed the impact of the transmitter array orientation and provided an algorithm to compensate for the misalignment by estimating the orientation and mechanically re-aligning the receiver array in that direction. Furthermore, a general simplified communication link is numerically simulated, showing that tilt significantly impacts equalizer performance and overall spectral efficiency, and the compensation algorithm is able to restore system performance effectively. Additionally, by comparing different antenna arrangements, it is found that a triangular antenna arrangement has exceptional performance over a range of antenna spacing. Unlike other antenna arrangements, that require optimum antenna spacing, it does not require optimum but tolerates a range of deviations with little practical impact on system performance.Impact of Different Antenna Arrangements and Transmitter Tilt to D-Band LoS MIMO ChannelPukar Shakya, Leibniz Institute for High Performance Microelectronics

Complementary Coded Scrambling Hopping Multiple Access in the Downlink MIMO Channels

The rapid growth of wireless devices has significantly increased the demand for user capacity in communication systems while exacerbating interference challenges. Existing multiple access techniques often face challenges in user capacity, particularly under unfavorable channel conditions. To tackle this issue, we propose a complementary coded scrambling hopping multiple access (CCSHMA) scheme that is designed to address the high user capacity demands in low signal-to-interference-plus-noise ratio environments. Our scheme utilizes three-dimensional complementary codes to scramble signals across multiple domains to mitigate interference. Furthermore, we implement a grouped code-hopping scheme to improve the user capacity. Simulation results indicate that the CCSHMA scheme effectively improves user capacity and achieves a reduced bit error rate under poor channel conditions compared to multiple-input multiple-output orthogonal frequency division multiple access.Complementary Coded Scrambling Hopping Multiple Access in the Downlink MIMO ChannelsXiqing Liu, Beijing University of Posts and Telecommunications

An Efficient Low-complexity Algorithm for MI-based MIMO ISAC Beamforming Design

The paper investigates MIMO integrated sensing and communication (ISAC) systems with extended radar clutter interference. Different from previous work considering a point target scatterer, we focus on a more general scenario with an extended target scatterer. We aim to maximize the sensing mutual information (MI) while satisfying constraints on communication quality of service (QoS) and base station (BS) transmit power. The existing algorithm exhibits high computational complexity in the general scenario proposed in this paper. Hence, we propose an efficient low-complexity algorithm that employs a parallel optimization strategy and closed-form solutions to significantly reduce the complexity of each iteration. Moreover, we derive a lower bound on sensing MI, which is a function of transmit beampattern, and provide a sufficient condition for its tightness. Numerical results demonstrate the low complexity and good performance of our proposed algorithm compared to the existing benchmarks and validate the relationship between the lower bound and sensing MI.An Efficient Low-complexity Algorithm for MI-based MIMO ISAC Beamforming DesignJIN LI, southeast university; Gui Zhou, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Tantao Gong, Liu Nan, Southeast University

Latency-Aware Radio and Computational Resource Allocation for Industrial CF-mMIMO Networks

Advances in communication technologies, such as cell-free massive multi-input multiple-output architecture and edge computing, are expected to support diverse heterogeneous service requirements in industrial use cases. However, the diverse communication demands of Industry 4.0 use cases make it essential to efficiently manage both radio and computational resources. This paper introduces a novel approach that addresses these challenges through a two-step sequential optimization process. First, we propose a genetic algorithm-based radio resource allocation scheme from a scheduling perspective. A second sub-problem concerning computational resource allocation is also addressed sequentially. The numerical results demonstrate that our proposed algorithm significantly improves latency compliance and effectively reduces the percentage of packets that violate deadline constraints. Considering services with small packets and strict latency constraints, the proposed scheme achieves gains of 98.5% and 99.5% compared to the benchmarking schemes. Furthermore, the proposed solution has shown great adaptability to varying latency requirements and traffic with respect to state-of-the-art schemes.Latency-Aware Radio and Computational Resource Allocation for Industrial CF-mMIMO NetworksSanyasi Vishnu Vardhan Rachuri, Aalborg University

Channel Recovery for Asymmetrical Uplink and Downlink Transceivers in Massive MIMO Systems...

The asymmetrical transceiver architecture has shown potential in reducing hardware complexity and cost in massive multiple-input multiple-output (MIMO) systems. However, channel recovery is necessary for the asymmetrical transceiver to acquire excellent transmission performance. Although the uniform planar array (UPA) is widely deployed in practical systems, the compact form of UPAs will result in high computational complexity due to the inherent high-dimensional nature of the array. The angle ambiguity problem also arises when directly extending the uniform linear array channel recovery method into the UPAs. To address these challenges, we propose a low-complexity gridless channel recovery method in this paper. First, the concept of the mixed angle for UPAs is introduced to deal with the low elevation angular resolution originating from the compact array form. After that, an antenna selection algorithm is designed to construct a virtual array based on the mixed angle to maximize the virtual array aperture with a minimal number of antennas. Finally, a low-complexity UPA-based modified newtonized orthogonal matching pursuit channel recovery algorithm is developed to mitigate angle ambiguity, thus enabling accurate reconstruction of the full downlink channel state information. Numerical results demonstrate the superiority of the proposed method in significantly reducing the number of receive uplink radio frequency chains while ensuring satisfactory channel recovery performance.Channel Recovery for Asymmetrical Uplink and Downlink Transceivers in Massive MIMO Systems with UPAsDahong Du, Xi Yang, East China Normal University; Ting Liu, nanjing university of information science and technology; Qing Xue, Chongqing University of Posts and Telecommunications

Channel Estimation in Massive MIMO-OFDM with Multi-group Adjustable Phase Shift Pilots

Estimating massive multiple-input multiple-output - orthogonal frequency division multiplexing (MIMO-OFDM) channels with low pilot overhead presents a significant challenge. Leveraging channel sparsity and pilot argument information (PAI), we propose a multi-group adjustable phase shift pilot (MAPSP) channel estimation method aimed at reducing pilot overhead. We first introduce a sparse channel model in angle-delay domain. Then, we propose the approach of generating phase shift pilots by dividing user terminals (UT) into groups and explore channel estimation based on the sparse channel model. We demonstrate that pilot interference can be mitigated by phase scheduling and received signal pre-processing. Capitalizing on this property, we propose a MAPSP implementation scheme. Simulation results indicate that the proposed MAPSP technique achieves a lower mean square error (MSE) of estimation than APSP and significantly enhances spectral efficiency.Channel Estimation in Massive MIMO-OFDM with Multi-group Adjustable Phase Shift PilotsYu Zhao, Li You, Jinke Tang, Mengyu Qian, Bin Jiang, Xiqi Gao, Southeast University

Adaptive Precoding Mode Selection Based on Triple-CSI for mmWave Massive MIMO Systems

To enhance spectral efficiency, massive MIMO, hybrid precoding (HP) and non-orthogonal multiple access (NOMA), etc. are emerged. However, these enablers need to match with their appropriate application scenarios to achieve expected performances. To this end, this paper proposes an adaptive precoding mode selection (APMS) approach for millimeter wave (mmWave) systems. Firstly, we explore the criteria for precoding mode selection and adaptively group users based on proposed triple-channel state information (triple-CSI) that consists of three parameters. Then, phase-minimum-mean-square error (PMMSE) algorithm is proposed to optimize the sum-rate of HP, and equal-gain-transmission (EGT) method is used for NOMA. Simulation results show that APMS significantly outperforms HP and NOMA. At 26 dBm transmission power and 64 physical antennas, the sum-rate of AMPS is improved by 28.2% and 38.0% compared to HP and NOMA, respectively.Adaptive Precoding Mode Selection Based on Triple-CSI for mmWave Massive MIMO SystemsJinghao Zhang, Hongcheng Zhuang, Lin Zhang, Sun Yat-sen University

PCI Planning with Reassignment Budget in Ultra-Dense Networks

The physical cell identity (PCI) is a critical parameter in wireless networks, enabling user equipment (UE) to uniquely identify cells and mitigate inter-cell interference during communication. However, the proliferation of base stations in modern networks has complicated the proper assignment of PCIs, as it requires avoiding multiple types of PCI conflicts, such as PCI collision, mod-3 collision, and confusion. Existing methods generally focus on addressing single PCI conflicts and involve reassigning PCIs for all cells, which can lead to significant data exchange overhead in large-scale networks. To address these challenges, this paper tackles the PCI planning problem by introducing a PCI reassignment budget as a constraint to minimize overall network interference. A penalty-based double-loop PCI planning algorithm is proposed, where the outer loop optimizes penalty parameters derived from the constraints, and the inner loop combines a block coordinate descent (BCD) approach with a proposed concave-convex procedure to optimize PCI assignments. Simulation results demonstrate the effectiveness of the proposed method in significantly reducing network interference compared to existing approaches.PCI Planning with Reassignment Budget in Ultra-Dense NetworksZihao Chen, Southern University of Science and Technology; Fan Xu, Tongji University; Yunlong Cai, Zhejiang University; Fan Liu, National Mobile Communications Research Lab., Southeast University; Jiaqiang Wen, Southern University of Science and Technology

Multi-Agent Reinforcement Learning Based Radio Resource Allocation for Video Transmission ...

The demand for large-capacity, latency sensitive applications such as ultra-high-definition video transmission is increasing in wireless communication systems. In the next-generation wireless local area network (LAN), multi-access point (AP) coordination technique is attracting attention for the purpose of reducing collision probability and improving throughput. In high-demand applications such as ultra-high-definition video transmission, there is a problem that increasing the capacity of wireless communication does not necessarily lead directly to their successful implementation. To accommodate more applications, it is important to enhance video throughput calculated based on whether the requirements for ultra-high-definition video transmission are satisfied. Considering the requirements of video transmission, when multi-APs coordinate and allocate each station (STA) to a radio resource, the number of patterns of when and to which STAs resources should be allocated becomes enormous. As a result, it is difficult to find an optimal resource allocation. Therefore, we propose a radio resource allocation for video transmission, utilizing reinforcement learning (RL). We consider two kinds of RL, which are single agent reinforcement learning (SARL) and multi-agent reinforcement learning (MARL). Our computer simulations demonstrate that MARL can achieve performance comparable to that of SARL while requiring minimal information exchange.Multi-Agent Reinforcement Learning Based Radio Resource Allocation for Video Transmission in Multi-AP TransmissionRyota Yamada, Osamu Nakamura, Hiromichi Tomeba, Yasuhiro Hamaguchi, Sharp Corporation