Reliability and User-Plane Latency Analysis of mmWave Massive MIMO for Grant-Free URLLC Applications

Evangelista, Joao V. C.; Kaddoum, Georges; Sattar, Zeeshan

doi:10.48550/arxiv.2107.08151

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…In addition, HetNet systems will support the massive connectivity of mobile and portable appliances and allow relaxation in the functionality of networks, with rapidly increasing higher bandwidth applications for users over the next decade. The potential benefits of mMIMO systems are the underlying the timely acquisition of CSI, either in the form of long-term statistical or instantaneous data (for each antenna node) [152,153]. Since the overall latency is formed via the transmission, processing, signaling, and retransmission of data packets, the initial channel request, training signals, scheduling, and queuing latencies add enormous weight when establishing a link between the BS and smart node functions [154,155].…”

Section: Discussionmentioning

confidence: 99%

A Study on Multi-Antenna and Pertinent Technologies with AI/ML Approaches for B5G/6G Networks

et al. 2022

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The quantum leap in mobile data traffic and high density of wireless electronic devices, coupled with the advancements in industrial radio monitoring and autonomous systems, have created great challenges for smooth wireless network operations. The fifth-generation and beyond (B5G) (also being referred to as sixth-generation (6G)) wireless communication technologies, due to their compatibility with the previous generations, are expected to overcome these unparalleled challenges. Accompanied by traditional and new techniques, the massive multiple input multiple output (mMIMO) approach is one of the evolving technologies for B5G/6G systems used to control the ever-increasing user stipulations and the emergence of new cases efficiently. However, the major challenges in deploying mMIMO systems are their high computational intricacy and high computing time latencies, as well as difficulties in fully exploiting the multi-antenna multi-frequency channels. Therefore, to optimize the current and B5G/6G wireless network elements proficiently, the use of the mMIMO approach in a HetNet structure with artificial intelligence (AI) techniques, e.g., machine learning (ML), distributed learning, federated learning, deep learning, and neural networks, has been considered as the prospective efficient solution. This work analyzes the observed problems and their AI/ML-enabled mitigation techniques in different mMIMO deployment scenarios for 5G/B5G networks. To provide a complete insight into the mMIMO systems with emerging antenna and propagation precoding techniques, we address and identify various relevant topics in each section that may help to make the future wireless systems robust. Overall, this work is designed to guide all B5G/6G stakeholders, including researchers and operators, aiming to understand the functional behavior and associated techniques to make such systems more agile for future communication purposes.

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