Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO Networks
- URL: http://arxiv.org/abs/2411.19020v1
- Date: Thu, 28 Nov 2024 09:48:52 GMT
- Title: Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO Networks
- Authors: Atchutaram K. Kocharlakota, Sergiy A. Vorobyov, Robert W. Heath Jr,
- Abstract summary: This paper introduces the pilot contamination-aware power control (PAPC) transformer neural network.
PAPC integrates pilot allocation data into the network, effectively handling pilot contamination scenarios.
Trained in an unsupervised learning framework, PAPC is evaluated against the accelerated proximal gradient (APG) algorithm.
- Score: 45.487183737784086
- License:
- Abstract: Learning-based downlink power control in cell-free massive multiple-input multiple-output (CFmMIMO) systems offers a promising alternative to conventional iterative optimization algorithms, which are computationally intensive due to online iterative steps. Existing learning-based methods, however, often fail to exploit the intrinsic structure of channel data and neglect pilot allocation information, leading to suboptimal performance, especially in large-scale networks with many users. This paper introduces the pilot contamination-aware power control (PAPC) transformer neural network, a novel approach that integrates pilot allocation data into the network, effectively handling pilot contamination scenarios. PAPC employs the attention mechanism with a custom masking technique to utilize structural information and pilot data. The architecture includes tailored preprocessing and post-processing stages for efficient feature extraction and adherence to power constraints. Trained in an unsupervised learning framework, PAPC is evaluated against the accelerated proximal gradient (APG) algorithm, showing comparable spectral efficiency fairness performance while significantly improving computational efficiency. Simulations demonstrate PAPC's superior performance over fully connected networks (FCNs) that lack pilot information, its scalability to large-scale CFmMIMO networks, and its computational efficiency improvement over APG. Additionally, by employing padding techniques, PAPC adapts to the dynamically varying number of users without retraining.
Related papers
- USEFUSE: Utile Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks [0.6435156676256051]
This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic.
An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption.
Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency.
arXiv Detail & Related papers (2024-12-18T11:04:58Z) - Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks [0.0]
We propose a proposed machine-learning supported approach to model predictive control.
We propose approximating part of the problem horizon, while maintaining safety guarantees.
The proposed MPC scheme can be applied to a wide range of applications, including those requiring a rapid control response.
arXiv Detail & Related papers (2024-08-19T08:13:37Z) - Multiagent Reinforcement Learning with an Attention Mechanism for
Improving Energy Efficiency in LoRa Networks [52.96907334080273]
As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions.
We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa)
Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
arXiv Detail & Related papers (2023-09-16T11:37:23Z) - Unsupervised Deep Unfolded PGD for Transmit Power Allocation in Wireless
Systems [0.6091702876917281]
We propose a simple low-complexity TPC algorithm based on the deep unfolding of the iterative projected gradient (PGD) algorithm into layers of a deep neural network and learning the step-size parameters.
Performance evaluation in dense device-to-device (D2D) communication scenarios showed that the proposed method can achieve better performance than the iterative algorithm with more than a factor of 2 lower number of iterations.
arXiv Detail & Related papers (2023-06-20T19:51:21Z) - AI-based Radio and Computing Resource Allocation and Path Planning in
NOMA NTNs: AoI Minimization under CSI Uncertainty [23.29963717212139]
We develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs)
It is shown that task scheduling significantly reduces the average AoI.
It is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users.
arXiv Detail & Related papers (2023-05-01T11:52:15Z) - Fair and Efficient Distributed Edge Learning with Hybrid Multipath TCP [62.81300791178381]
The bottleneck of distributed edge learning over wireless has shifted from computing to communication.
Existing TCP-based data networking schemes for DEL are application-agnostic and fail to deliver adjustments according to application layer requirements.
We develop a hybrid multipath TCP (MP TCP) by combining model-based and deep reinforcement learning (DRL) based MP TCP for DEL.
arXiv Detail & Related papers (2022-11-03T09:08:30Z) - Over-the-Air Federated Multi-Task Learning via Model Sparsification and
Turbo Compressed Sensing [48.19771515107681]
We propose an over-the-air FMTL framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server.
In OA-FMTL, the local updates of edge devices are sparsified, compressed, and then sent over the uplink channel in a superimposed fashion.
We analyze the performance of the proposed OA-FMTL framework together with the M-Turbo-CS algorithm.
arXiv Detail & Related papers (2022-05-08T08:03:52Z) - Turning Channel Noise into an Accelerator for Over-the-Air Principal
Component Analysis [65.31074639627226]
Principal component analysis (PCA) is a technique for extracting the linear structure of a dataset.
We propose the deployment of PCA over a multi-access channel based on the algorithm of gradient descent.
Over-the-air aggregation is adopted to reduce the multi-access latency, giving the name over-the-air PCA.
arXiv Detail & Related papers (2021-04-20T16:28:33Z) - Federated Learning on the Road: Autonomous Controller Design for
Connected and Autonomous Vehicles [109.71532364079711]
A new federated learning (FL) framework is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs)
A novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, and the unbalanced and nonindependent and identically distributed data across CAVs.
A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal controller.
arXiv Detail & Related papers (2021-02-05T19:57:47Z)
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.