Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation

• URL: http://arxiv.org/abs/2003.05685v1
• Date: Thu, 12 Mar 2020 10:00:08 GMT
• Title: Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation
• Authors: Hamza Khan, M. Majid Butt, Sumudu Samarakoon, Philippe Sehier, and Mehdi Bennis
• Abstract summary: We propose a deep learning assisted CSI estimation technique in highly mobile vehicular networks. We formulate and solve a dynamic network slicing based resource allocation problem for vehicular user equipments.
• Score: 36.364156900974535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G) and beyond wireless communication systems owing to higher spectrum efficiency, spatial gains, and energy efficiency. Reaping the benefits of MIMO transmission can be fully harnessed if the channel state information (CSI) is available at the transmitter side. However, the acquisition of transmitter side CSI entails many challenges. In this paper, we propose a deep learning assisted CSI estimation technique in highly mobile vehicular networks, based on the fact that the propagation environment (scatterers, reflectors) is almost identical thereby allowing a data driven deep neural network (DNN) to learn the non-linear CSI relations with negligible overhead. Moreover, we formulate and solve a dynamic network slicing based resource allocation problem for vehicular user equipments (VUEs) requesting enhanced mobile broadband (eMBB) and ultra-reliable low latency (URLLC) traffic slices. The formulation considers a threshold rate violation probability minimization for the eMBB slice while satisfying a probabilistic threshold rate criterion for the URLLC slice. Simulation result shows that an overhead reduction of 50% can be achieved with 12% increase in threshold violations compared to an ideal case with perfect CSI knowledge.

Related papers

• Generative MIMO Beam Map Construction for Location Recovery and Beam Tracking [67.65578956523403]
  This paper proposes a generative framework to recover location labels directly from sparse channel state information (CSI) measurements.<n>Instead of directly storing raw CSI, we learn a compact low-dimensional radio map embedding and leverage a generative model to reconstruct the high-dimensional CSI.<n> Numerical experiments demonstrate that the proposed model can improve localization accuracy by over 30% and achieve a 20% capacity gain in non-line-of-sight (NLOS) scenarios.
  arXiv  Detail & Related papers  (2025-11-21T07:25:49Z)
• FGGM: Formal Grey-box Gradient Method for Attacking DRL-based MU-MIMO Scheduler [38.931160910894036]
  In 5G mobile communication systems, MU-MIMO has been applied to enhance spectral efficiency and support high data rates.<n>To maximize spectral efficiency while providing fairness among users, the base station needs to selects a subset of users for data transmission.<n>This paper investigates how a group of adversarial users can exploit unsanitized raw CSIs to launch a throughput degradation attack.
  arXiv  Detail & Related papers  (2025-10-30T02:17:32Z)
• Green Learning for STAR-RIS mmWave Systems with Implicit CSI [53.03358325565645]
  Green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) broadcasting systems.<n>Motivated by the emphasis on environmental sustainability in future 6G networks, this work adopts a transmission framework for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption.
  arXiv  Detail & Related papers  (2025-09-08T15:56:06Z)
• Distributed Gossip-GAN for Low-overhead CSI Feedback Training in FDD mMIMO-OFDM Systems [65.23921727688749]
  We propose a novel gossiping generative adversarial network (Gossip-GAN)-aided CSI feedback training framework.<n>Gossip-GAN enables the CSI feedback training with low-overhead while preserving users' privacy.
  arXiv  Detail & Related papers  (2025-08-31T07:46:16Z)
• Standards-Compliant DM-RS Allocation via Temporal Channel Prediction for Massive MIMO Systems [4.251030047034567]
  We introduce the concept of channel prediction-based reference signal allocation (CPRS)<n>CPRS jointly optimize channel prediction and DM-RS allocation to improve data throughput without requiring CSI feedback.<n>We show up to 36.60% throughput improvement over benchmark strategies.
  arXiv  Detail & Related papers  (2025-07-15T07:56:37Z)
• Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach [65.47969413708344]
  We introduce the concept of CF twins and design a conditional generative diffusion model (CGDM)<n>We employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CF conditioned on the coarse-grained CF.<n>We show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines.
  arXiv  Detail & Related papers  (2025-05-12T01:36:06Z)
• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate. Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse [56.384390765357004]
  We propose an integrated federated split learning and hyperdimensional computing framework for emerging foundation models. This novel approach reduces communication costs, computation load, and privacy risks, making it suitable for resource-constrained edge devices in the Metaverse.
  arXiv  Detail & Related papers  (2024-08-26T17:03:14Z)
• A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems [45.22132581755417]
  Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, obtaining CSI through feedback from the user equipment (UE) becomes challenging with the increasing scale of antennas and subcarriers. Deep learning-based methods have emerged for compressing CSI but these methods require substantial collected samples. Existing deep learning methods also suffer from dramatically growing feedback overhead owing to their focus on full-dimensional CSI feedback. We propose a low-overhead-Extrapolation based Few-Shot CSI
  arXiv  Detail & Related papers  (2023-12-07T06:01:47Z)
• Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO [56.022764337221325]
  Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging. This paper proposes a deep learning (DL)-based rate-splitting multiple access scheme for RIS-aided Tera-Hertz multi-user multiple access systems.
  arXiv  Detail & Related papers  (2022-09-18T03:07:37Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• CSI Feedback with Model-Driven Deep Learning of Massive MIMO Systems [0.0]
  We propose a two stages low rank (TSLR) CSI feedback scheme to reduce the feedback overhead based on model-driven deep learning. Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback.
  arXiv  Detail & Related papers  (2021-12-13T03:50:43Z)
• Deep Learning-based Implicit CSI Feedback in Massive MIMO [68.81204537021821]
  We propose a DL-based implicit feedback architecture to inherit the low-overhead characteristic, which uses neural networks (NNs) to replace the precoding matrix indicator (PMI) encoding and decoding modules. For a single resource block (RB), the proposed architecture can save 25.0% and 40.0% of overhead compared with Type I codebook under two antenna configurations.
  arXiv  Detail & Related papers  (2021-05-21T02:43:02Z)
• Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System [22.068682756598914]
  A novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance. The elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks.
  arXiv  Detail & Related papers  (2021-05-01T22:50:25Z)
• CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback [7.63185216082836]
  This paper presents a novel neural network CLNet tailored for CSI feedback problem based on the intrinsic properties of CSI. The experiment result shows that CLNet outperforms the state-of-the-art method by average accuracy improvement of 5.41% in both outdoor and indoor scenarios.
  arXiv  Detail & Related papers  (2021-02-15T12:16:11Z)
• Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning [119.97450366894718]
  A novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station. A channel estimation approach is developed to measure the channel state information (CSI) in real-time. A distributional reinforcement learning (DRL) approach is proposed to learn the optimal IR reflection and maximize the expectation of downlink capacity.
  arXiv  Detail & Related papers  (2020-02-24T22:18:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.