7 research outputs found
Transformer-Aided CSI Prediction for Interference Alignment in MIMO Systems
No full textAbstract
Interference alignment (IA) is a degrees-of-freedom optimal interference management technique which approaches the capacity of an interference network at high signal-to-noise ratio (SNR) regime. It works as a cooperative precoding scheme in which different transmitters coordinate their transmissions so that all interference signals at an unintended receiver are confined to the same subspace. Each receiver can recover the desired signal by eliminating the aligned interferences using a suitable receiver design. However, IA requires full channel state information (CSI) of all involved links at all transmitters, which incurs a huge feedback overhead and is not feasible in practice. In this study, we propose a transformer-aided CSI prediction for signaling overhead reduction. Our proposed transformerbased CSI prediction demonstrates superior accuracy compared to the conventional methods. The predicted CSI is utilized to perform IA and compute the precoding matrices in a multiantenna system, allowing them to efficiently implement IA scheme. Numerical results show that the achievable rate with the proposed transformer-based method is within 98 % of the achievable rate with perfect CSI. In comparison, conventional deep learning-based CSI estimation approaches, namely long short-term memory networks and convolutional neural networks, is found to achieve only 90 % and 88 % of the ideal rate, respectively.Abstract
Interference alignment (IA) is a degrees-of-freedom optimal interference management technique which approaches the capacity of an interference network at high signal-to-noise ratio (SNR) regime. It works as a cooperative precoding scheme in which different transmitters coordinate their transmissions so that all interference signals at an unintended receiver are confined to the same subspace. Each receiver can recover the desired signal by eliminating the aligned interferences using a suitable receiver design. However, IA requires full channel state information (CSI) of all involved links at all transmitters, which incurs a huge feedback overhead and is not feasible in practice. In this study, we propose a transformer-aided CSI prediction for signaling overhead reduction. Our proposed transformerbased CSI prediction demonstrates superior accuracy compared to the conventional methods. The predicted CSI is utilized to perform IA and compute the precoding matrices in a multiantenna system, allowing them to efficiently implement IA scheme. Numerical results show that the achievable rate with the proposed transformer-based method is within 98 % of the achievable rate with perfect CSI. In comparison, conventional deep learning-based CSI estimation approaches, namely long short-term memory networks and convolutional neural networks, is found to achieve only 90 % and 88 % of the ideal rate, respectively
A Deep-Unfolding Approach to RIS Phase Shift Optimization Via Transformer-Based Channel Prediction
No full textAbstract
Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution that can provide dynamic control over the propagation of electromagnetic waves. The RIS technology is envisioned as a key enabler of sixth-generation networks by offering the ability to adaptively manipulate signal propagation through the smart configuration of its phase shift coefficients, thereby optimizing signal strength, coverage, and capacity. However, the realization of this technology's full potential hinges on the accurate acquisition of channel state information (CSI). In this paper, we propose an efficient CSI prediction framework for a RIS-assisted communication system based on the machine learning (ML) transformer architecture. Architectural modifications are introduced to the vanilla transformer for multivariate time series forecasting to achieve high prediction accuracy. The predicted channel coefficients are then used to optimize the RIS phase shifts. Simulation results present a comprehensive analysis of key performance metrics, including data rate and outage probability. Our results confirm the effectiveness of the proposed ML approach and demonstrate its superiority over other baseline ML-based CSI prediction schemes such as conventional deep neural networks and long short-term memory architectures, albeit at the cost of slightly increased complexity.Abstract
Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution that can provide dynamic control over the propagation of electromagnetic waves. The RIS technology is envisioned as a key enabler of sixth-generation networks by offering the ability to adaptively manipulate signal propagation through the smart configuration of its phase shift coefficients, thereby optimizing signal strength, coverage, and capacity. However, the realization of this technology's full potential hinges on the accurate acquisition of channel state information (CSI). In this paper, we propose an efficient CSI prediction framework for a RIS-assisted communication system based on the machine learning (ML) transformer architecture. Architectural modifications are introduced to the vanilla transformer for multivariate time series forecasting to achieve high prediction accuracy. The predicted channel coefficients are then used to optimize the RIS phase shifts. Simulation results present a comprehensive analysis of key performance metrics, including data rate and outage probability. Our results confirm the effectiveness of the proposed ML approach and demonstrate its superiority over other baseline ML-based CSI prediction schemes such as conventional deep neural networks and long short-term memory architectures, albeit at the cost of slightly increased complexity
Novel Learning-Based Multi-User Detection Algorithms for Spatially Correlated MTC
No full textAbstract
Emerging massive machine-type communications service class needs to support many devices while ensuring that scarce radio resources are utilized efficiently. Non-orthogonal multiple access is proposed to minimize the signaling overhead and optimize resource allocation. However, during the initial access, the base station is presented with the challenge of identifying sparsely active devices in the absence of knowledge about the sparsity and channel state information. The user channels in most practical scenarios have common reflection paths, making them partially correlated, which can be exploited to improve the detection performance at the base station. In this context, we formulate a novel multi-user detection (MUD) problem in spatially correlated Rician channels, which we reformulate as a multi-label classification problem utilizing deep learning techniques. We propose two diverse approaches to tackle this problem: ViT-Net, a vision transformer-based architecture, and FAR-Net, a fully activated deep neural network featuring residual connections. Our analysis highlights the significance of spatial correlation for MUD, which can accord around 13% higher overloading ratio compared to the non-correlated scenario. Numerical evaluations demonstrate the effectiveness of the proposed model in addressing spatial correlation compared to the existing deep learning models.Abstract
Emerging massive machine-type communications service class needs to support many devices while ensuring that scarce radio resources are utilized efficiently. Non-orthogonal multiple access is proposed to minimize the signaling overhead and optimize resource allocation. However, during the initial access, the base station is presented with the challenge of identifying sparsely active devices in the absence of knowledge about the sparsity and channel state information. The user channels in most practical scenarios have common reflection paths, making them partially correlated, which can be exploited to improve the detection performance at the base station. In this context, we formulate a novel multi-user detection (MUD) problem in spatially correlated Rician channels, which we reformulate as a multi-label classification problem utilizing deep learning techniques. We propose two diverse approaches to tackle this problem: ViT-Net, a vision transformer-based architecture, and FAR-Net, a fully activated deep neural network featuring residual connections. Our analysis highlights the significance of spatial correlation for MUD, which can accord around 13% higher overloading ratio compared to the non-correlated scenario. Numerical evaluations demonstrate the effectiveness of the proposed model in addressing spatial correlation compared to the existing deep learning models
Decomposition Based Interference Management Framework for Local 6G Networks
No full textAbstract
Managing inter-cell interference is among the major challenges in a wireless network, more so when strict quality of service needs to be guaranteed such as in ultra-reliable low latency communications (URLLC) applications. This study introduces a novel intelligent interference management framework for a local 6G network that allocates resources based on interference prediction. The proposed algorithm involves an advanced signal pre-processing technique known as empirical mode decomposition followed by prediction of each decomposed component using the sequence-to-one transformer algorithm. The predicted interference power is then used to estimate future signal-to-interference plus noise ratio, and subsequently allocate resources to guarantee the high reliability required by URLLC applications. Finally, an interference cancellation scheme is explored based on the predicted interference signal with the transformer model. The proposed sequence-to-one transformer model exhibits its robustness for interference prediction. The proposed scheme is numerically evaluated against two baseline algorithms, and is found that the root mean squared error is reduced by up to 55% over a baseline scheme.Abstract
Managing inter-cell interference is among the major challenges in a wireless network, more so when strict quality of service needs to be guaranteed such as in ultra-reliable low latency communications (URLLC) applications. This study introduces a novel intelligent interference management framework for a local 6G network that allocates resources based on interference prediction. The proposed algorithm involves an advanced signal pre-processing technique known as empirical mode decomposition followed by prediction of each decomposed component using the sequence-to-one transformer algorithm. The predicted interference power is then used to estimate future signal-to-interference plus noise ratio, and subsequently allocate resources to guarantee the high reliability required by URLLC applications. Finally, an interference cancellation scheme is explored based on the predicted interference signal with the transformer model. The proposed sequence-to-one transformer model exhibits its robustness for interference prediction. The proposed scheme is numerically evaluated against two baseline algorithms, and is found that the root mean squared error is reduced by up to 55% over a baseline scheme
Wasiilah Google Sites Fii tadriis Maharah Al-Qira’ah As Samitha Fii Al-Madrasah Al-Aliyah Al-Hukumiyah 2 Kuantan Singingi
No full textThe main problem in this research is “does media Google Sites influence in learning silent reading skills on student eleventh graders of Madrasah Aliyah Negeri 2 Kuantan Singingi?”. This research is very broad, the author limits the first is the ability to silent reading skills before using media Google Sites. second is the using of media Google Sites in learning silent reading skills. third is the influence of media Google Sites in learning silent reading skills. The aims of this research is to describe ability silent reading skills before using media Goole Sites, to describe using media Google Sites, to describe influence media Google Sites in improving ability silent reading skills on student eleventh graders. The research method used is quantitative study approach that is quasi eksperimen mothod and analytic data use SPSS. The result in this research is first the ability before use media Google Sites in control class is 69, and eksperimental class is 68,33. second is the using media Google Sites in three steps. first is opening, second is mian activity its five steps observing, asking, trying, associating, and communicating, third is closing. third is the influence media Google Sites in eksperimental class is 81,66 and control class is 71,33. and based on result of Paired Sample T test, T table 3,460 bigger than T count 2,043. and sig(2-Tailed) 0,005 bigger than 0,000. its mean Median oogle Sites is influence to ability silent reading skills on student eleventh graders of Madrasah Aliyah Negeri 2 Kuantan Singing
