792 research outputs found
Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance
The pervasive nature of wireless telecommunication has made it the foundation
for mainstream technologies like automation, smart vehicles, virtual reality,
and unmanned aerial vehicles. As these technologies experience widespread
adoption in our daily lives, ensuring the reliable performance of cellular
networks in mobile scenarios has become a paramount challenge. Beamforming, an
integral component of modern mobile networks, enables spatial selectivity and
improves network quality. However, many beamforming techniques are iterative,
introducing unwanted latency to the system. In recent times, there has been a
growing interest in leveraging mobile users' location information to expedite
beamforming processes. This paper explores the concept of contextual
beamforming, discussing its advantages, disadvantages and implications.
Notably, the study presents an impressive 53% improvement in signal-to-noise
ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared
to scenarios without beamforming. It further elucidates how MRT contributes to
contextual beamforming. The importance of localization in implementing
contextual beamforming is also examined. Additionally, the paper delves into
the use of artificial intelligence schemes, including machine learning and deep
learning, in implementing contextual beamforming techniques that leverage user
location information. Based on the comprehensive review, the results suggest
that the combination of MRT and Zero forcing (ZF) techniques, alongside deep
neural networks (DNN) employing Bayesian Optimization (BO), represents the most
promising approach for contextual beamforming. Furthermore, the study discusses
the future potential of programmable switches, such as Tofino, in enabling
location-aware beamforming
Multi-layer Utilization of Beamforming in Millimeter Wave MIMO Systems
mmWave frequencies ranging between (30-300GHz) have been considered the perfect solution to the scarcity of bandwidth in the traditional sub-6GHz band and to the ever increasing demand of many emerging applications in today\u27s era. 5G and beyond standards are all considering the mmWave as an essential part of there networks. Beamforming is one of the most important enabling technologies for the mmWave to compensate for the huge propagation lose of these frequencies compared to the sub-6GHz frequencies and to ensure better spatial and spectral utilization of the mmWave channel space. In this work, we tried to develop different techniques to improve the performance of the systems that use mmWave. In the physical layer, we suggested several hybrid beamforming architectures that both are relatively simple and spectrally efficient by achieving fully digital like spectral efficiency (bits/sec/Hz). For the mobility management, we derived the expected degradation that can affect the performance of a special type of beamforming that is called the Random Beamforming (RBF) and optimized the tunable parameters for such systems when working in different environments. Finally, in the networking layer, we first studied the effect of using mmWave frequencies on the routing performance comparing to the performance achieved when using sub-6 GHz frequencies. Then we developed a novel opportunistic routing protocol for Mobile Ad-Hoc Networks (MANET) that uses a modified version of the Random Beamforming (RBF) to achieve better end to end performance and to reduce the overall delay in delivering data from transmitting nodes to the intended receiving nodes. From all these designs and studies, we conclude that mmWave frequencies and their enabling technologies (i.e. Beamforming, massive MIMO, ...etc.) are indeed the future of wireless communicatons in a high demanding world of Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), and self driving cars
Deep Learning Aided Parametric Channel Covariance Matrix Estimation for Millimeter Wave Hybrid Massive MIMO
Millimeter-wave (mmWave) channels, which occupy frequency ranges much higher
than those being used in previous wireless communications systems, are utilized
to meet the increased throughput requirements that come with 5G communications.
The high levels of attenuation experienced by electromagnetic waves in these
frequencies causes MIMO channels to have high spatial correlation. To attain
desirable error performances, systems require knowledge about the channel
correlations. In this thesis, a deep neural network aided method is proposed
for the parametric estimation of the channel covariance matrix (CCM), which
contains information regarding the channel correlations. When compared to some
methods found in the literature, the proposed method yields satisfactory
performance in terms of both computational complexity and channel estimation
errors.Comment: M.Sc. Thesis, published at:
https://open.metu.edu.tr/handle/11511/9319
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