Towards More Reliable Deep Learning-Based Link Adaptation for WiFi 6

Hussien, Mostafa; Ahmed, Mohammed; Dahman, Ghassan; Nguyen, Kim Khoa; Cheriet, Mohamed; Poitau, Gwenael

doi:10.1109/icc42927.2021.9500828

Cited by 12 publications

(4 citation statements)

References 11 publications

(2 reference statements)

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“…Recently, state-of-the-art ML algorithms go all the way from data mining techniques, and resource allocation problems to tackle most of the issues in cellular networks. Interestingly, there is a significant trend for implementing powerful ML-based solutions for many complex problems in wireless communications such as link adaptation [4], resource allocation [5], Channel State Information (CSI) compression [6], beamforming [7], among others. ML algorithms can be categorized into three main categories namely supervised, unsupervised, and Reinforcement Learning (RL).…”

Section: Introductionmentioning

confidence: 99%

Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

Hussien¹,

Abdelmoaty²,

Elsaadany³

et al. 2023

IEEE Access

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The Beyond fifth Generation (B5G) communication systems imposed several challenges on radio designers. For example, a machine is required to set up a call at a low Signal-to-Noise Ratio (SNR), as low as -10 dB, in the extended coverage mode. Moreover, only one receive antenna will be available, and virtually no frequency diversity. Such requirements present major challenges to maintaining timing and frequency synchronization. Carrier Frequency Offset (CFO) estimation is at the heart of these challenges. Different ways have been proposed for CFO estimation such as maximum likelihood based on a cyclic prefix. Nevertheless, these methods remain limited in various ways. At the same time, Machine Learning (ML) techniques showed outstanding performance in several wireless communication problems. In this work, we propose an ML-based approach for CFO estimation in OFDM systems. Specifically, we propose a Gradient-Boosting Machine (GBM)-based solution to predict the CFO given the received Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). Furthermore, we make our dataset available for public access to encourage other researchers to pursue this promising direction. We compare our results with different baseline models (i.e., artificial neural networks and support vector machines). The experimental results show that our model outperforms other baseline models due to its ensemble nature which enables ensemble models to obtain a better generalization behavior.INDEX TERMS 5G, carrier frequency offset, gradient-boosting, machine learning, new radio.

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Section: Introductionmentioning

confidence: 99%

Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

Hussien¹,

Abdelmoaty²,

Elsaadany³

et al. 2023

IEEE Access

Self Cite

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“…Specifically, ML was a main player in the 5G and beyond communication systems. It has been adopted to provide modern communication systems with a plethora of intelligent services and functions such as intelligent resource allocation [7], [8], link adaptation [9], [10], beamforming [11],…”

Section: Introductionmentioning

confidence: 99%

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Hussien¹

2023

Preprint

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<p>The last decade, (2012 - 2022), saw an unprecedented advance in machine learning (ML) techniques, particularly deep learning (DL). As a result of the proven capabilities of DL, a large amount of work has been presented and studied in almost every field. Since 2012, when the convolution neural networks have been reintroduced in the context of \textit{ImagNet} competition, DL continued to achieve superior performance in many challenging tasks and problems. Wireless communications, in general, and medium access control (MAC) techniques, in particular, were among the fields that were heavily affected by this improvement. MAC protocols play a critical role in defining the performance of wireless communication systems. At the same time, the community lacks a comprehensive survey that collects, analyses, and categorizes the recent work in ML-inspired MAC techniques. In this work, we fill this gap by surveying a long line of work in this era. We solidify the impact of machine learning on wireless MAC protocols. We provide a comprehensive background to the widely adopted MAC techniques, their design issues, and their taxonomy, in connection with the famous application domains. Furthermore, we provide an overview of the ML techniques that have been considered in this context. Finally, we augment our work by proposing some promising future research directions and open research questions that are worth further investigation.</p>

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“…Thus, link adaptation plays a major key role in determining the system's latency, bit error performance, and maximum operating data rate. 13,15 There are several current-mode logic (CML) hybrid topologies [16][17][18] that have been reported to segregate the inbound signal from the signal on the interconnect for both off-chip and on-chip interconnects. Though the CML circuit topology is a popular choice for hybrid circuit realization because of its high-speed operation, they are power hungry due to its static power consumption.…”

Section: Introductionmentioning

confidence: 99%

“…It automatically tunes the transmitter and receiver settings to optimize the SI and bit error rate (BER). Thus, link adaptation plays a major key role in determining the system's latency, bit error performance, and maximum operating data rate 13,15 …”

Section: Introductionmentioning

confidence: 99%

An adaptive link training based hybrid circuit topology for full‐duplex on‐chip interconnects

Govindaswamy

Kammari

Pasupureddi

2023

Circuit Theory & Apps

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SummaryThis work proposes a link training based half‐rate dynamic current‐steering echo‐cancellation hybrid circuit topology for full‐duplex signaling that performs echo cancellation by deploying a bit error detection unit and backchannel adaptation. In addition, this work proposes a power‐efficient half‐rate dynamic current‐steering logic hybrid circuit for full‐duplex signaling over on‐chip interconnects for simultaneous transmission and reception. The half‐rate dynamic current‐steering hybrid circuit topology has low static power consumption compared with traditional current‐mode circuit topology implementations, thanks to the discrete nature of the current‐steering hybrid topology. The proposed scheme is implemented in 1.2 V, 65‐nm complementary metal‐oxide semiconductor (CMOS). The performance results show that the link training scheme sets the control bits for both the transmitter driver and hybrid circuit topology when the backchannel adaptation loop gets locked for 1‐ and 3‐mm interconnects for process, voltage, and temperature (PVT) variations. The proposed hybrid circuit topology is able to receive the required far‐end transmitted data with the link training scheme and has the differential recovered received signal voltage swing of 1.2 V for both 1‐ and 3‐mm interconnect at the 10‐Gb/s data rate. The total power consumption of the hybrid is 3 and 3.5 mW for 1‐ and 3‐mm interconnect with an energy efficiency of 0.6 and 0.7 pJ/bit, respectively, at 10‐Gb/s full‐duplex operation.

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Towards More Reliable Deep Learning-Based Link Adaptation for WiFi 6

Cited by 12 publications

References 11 publications

Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

An adaptive link training based hybrid circuit topology for full‐duplex on‐chip interconnects

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