Dealing with CSI Compression to Reduce Losses and Overhead: An Artificial Intelligence Approach

Shehzad, Muhammad Karam; Rose, Luca; Assaad, Mohamad

doi:10.1109/iccworkshops50388.2021.9473840

Cited by 10 publications

(12 citation statements)

References 8 publications

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“…• An RNN-based wireless channel predictor [5] is used in [3], explained in Section III-C, to deal with inaccurate channel state information (CSI).…”

Section: A Machine Learning At Phymentioning

confidence: 99%

“…In our study, summarized in Section II-A, we assumed two RNN-based twin channel predictors at the gNB and UE [3]. The past CSI is utilized for training the RNN at both ends of the communication system.…”

Section: Csi Feedbackmentioning

confidence: 99%

“…The CSI-RS can be enhanced by compressing the pilots and the PMI feedback exploiting ad hoc ML compressors. Furthermore, channel prediction techniques [5] can be used in order to pre-establish a baseline for the CSI feedback [3]. Other aspects that can be improved include frequency of pilots in both CSI-RS and DMRS, power and timing and CSI-RS port selection.…”

Section: A Csi Feedbackmentioning

confidence: 99%

“…6G will be operating in TeraHertz (THz) frequencies (0.1-10 THz), hence beneficial for multiple use cases in industrial applications, providing immense data rates (≈ 1 Tb/s), accelerating internet-of-things, and wider network coverage. AI/ML will pave the way for THz communications at different layers [2], e.g., supporting channel acquisition [3] and modulation classification [4] at PHY. Similarly, at the link layer, beamforming design and channel allocation can exploit ML [2].…”

Section: Introductionmentioning

confidence: 99%

“…In THz systems, a channel can significantly vary at a micrometer scale, resulting in a tremendous increase in channel estimation frequency and corresponding overhead. ML algorithms can counter this issue by using, e.g., improved channel prediction techniques [3], [5].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence for 6G Networks: Technology Advancement and Standardization

Shehzad,

Rose,

Butt

et al. 2022

Preprint

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With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks. 6G networks will be immensely complex, requiring more deployment time, cost and management efforts. On the other hand, mobile network operators demand these networks to be intelligent, self-organizing, and cost-effective to reduce operating expenses (OPEX). Machine learning (ML), a branch of artificial intelligence (AI), is the answer to many of these challenges providing pragmatic solutions, which can entirely change the future of wireless network technologies. By using some case study examples, we briefly examine the most compelling problems, particularly at the physical (PHY) and link layers in cellular networks where ML can bring significant gains. We also review standardization activities in relation to the use of ML in wireless networks and future timeline on readiness of standardization bodies to adapt to these changes. Finally, we highlight major issues in ML use in the wireless technology, and provide potential directions to mitigate some of them in 6G wireless networks.

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“…• An RNN-based wireless channel predictor [5] is used in [3], explained in Section III-C, to deal with inaccurate channel state information (CSI).…”

Section: A Machine Learning At Phymentioning

confidence: 99%

Section: Csi Feedbackmentioning

confidence: 99%