Fading Channel Prediction for 5G and 6G Mobile Communication Systems

Nowadays, there is a trend to employ adaptive so-lutions in mobile communication. The adaptive transmission sys-tems seem to answer the need for highly reliable communicationthat serves high data rates. For efficient adaptive transmission,the future Channel State Information (CSI) has to be known. Thevarious prediction methods can be applied to estimate the futureCSI. However, each method has its bottlenecks. The task is evenmore challenging while considering the future 5G/6G communi-cation where the employment of sub-6 GHz and millimetre waves(mmWaves) in narrow-band, wide-band and ultra-wide-bandtransmission is considered. Thus, we describe the differencesbetween sub-6 GHz/mmWave and narrow-band/wide-band/ultra-wide-band channel prediction, provide a comprehensive overviewof available prediction methods, discuss its performance andanalyse the opportunity to use them in sub-6 GHz and mmWavesystems. We select Long Short-Term Memory Recurrent NeuralNetwork (RNN) as the most promising technique for future CSIprediction and propose optimising one of its parameters - thenumber of input features, which was not yet considered as anopportunity to improve the performance of CSI prediction

Fading Channel Prediction for 5G and 6G Mobile Communication Systems

Nowadays, there is a trend to employ adaptive so-lutions in mobile communication. The adaptive transmission sys-tems seem to answer the need for highly reliable communicationthat serves high data rates. For efficient adaptive transmission,the future Channel State Information (CSI) has to be known. Thevarious prediction methods can be applied to estimate the futureCSI. However, each method has its bottlenecks. The task is evenmore challenging while considering the future 5G/6G communi-cation where the employment of sub-6 GHz and millimetre waves(mmWaves) in narrow-band, wide-band and ultra-wide-bandtransmission is considered. Thus, we describe the differencesbetween sub-6 GHz/mmWave and narrow-band/wide-band/ultra-wide-band channel prediction, provide a comprehensive overviewof available prediction methods, discuss its performance andanalyse the opportunity to use them in sub-6 GHz and mmWavesystems. We select Long Short-Term Memory Recurrent NeuralNetwork (RNN) as the most promising technique for future CSIprediction and propose optimising one of its parameters - thenumber of input features, which was not yet considered as anopportunity to improve the performance of CSI prediction

Fading Channel Prediction for 5G and 6G Mobile Communication Systems

Nowadays, there is a trend to employ adaptive solutions in mobile communication. The adaptive transmission systems seem to answer the need for highly reliable communication that serves high data rates. For efficient adaptive transmission, the future Channel State Information (CSI) has to be known. The various prediction methods can be applied to estimate the future CSI. However, each method has its bottlenecks. The task is even more challenging while considering the future 5G/6G communication where the employment of sub-6 GHz and millimetre waves (mmWaves) in narrow-band, wide-band and ultra-wide-band transmission is considered. Thus, author describes the differences between sub-6 GHz/mmWave and narrow-band/wide-band/ultra-wide-band channel prediction, provide a comprehensive overview of available prediction methods, discuss its performance and analyse the opportunity to use them in sub-6 GHz and mmWave systems. We select Long Short-Term Memory Recurrent Neural Network (RNN) as the most promising technique for future CSI prediction and propose optimising two of its parameters - the number of input features, which was not yet considered as an opportunity to improve the performance of CSI prediction, and the number of hidden layers

What is the Advantage of Cooperation in Self-Organizing Networks?

Abstract-Self-organizing network (SON) functions can be characterized by their required cooperation between the network elements (NEs). The cooperation among the NEs can include a multitude of possible actions, such as reporting of alarms or coordination of joint parameter modifications at multiple NEs. However, the question for the advantage of cooperation among the NEs in SONs is still an open research topic. By limiting the cooperation between the NEs, the required architectures for utilizing the SON function at hand can be simplified, which in turn can lead to cost savings. In this paper, we investigate the impact of degraded cooperation among the NEs on the SON architecture required and on the performance in a joint capacity and coverage optimization (CCO) use case. For the scenario investigated, we observe that the performance decreases dramatically when decreasing the cooperation among the NEs. However, we can also show that the exchange of information, such as the values of considered key performance indicators (KPIs), among the NEs is more important for an efficient operation than the coordination of the NE's actions. Our results show that, a centralized approach outperforms distributed and localized approaches for the CCO use case investigated

3-D High-Resolution ISAR Imaging for Noncooperative Air Targets

This article uses the inverse synthetic aperture radar (ISAR) imaging method to present real-world tests on 3-D radar imaging of noncooperative air targets. Initially, the fundamentals of 3-D ISAR are introduced. This is followed by a discussing of the challenges of obtaining high-quality 3-D radar images. An essential feature of the applied method is its basis on the back-projection family of techniques, eliminating the need for iterative image reconstruction. These theoretical concepts are validated using both simulations and real-life signals. This article also provides insights into the measurement campaign and the signal processing techniques applied to achieve the presented results