IRS-Empowered 6G Networks: Deployment Strategies, Performance Optimization, and Future Research Directions

Naeem, Faisal; Kaddoum, Georges; Khan, Saud; Khan, Komal S.; Adam, Nadir

doi:10.1109/access.2022.3220682

Cited by 14 publications

(6 citation statements)

References 132 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Resource scheduling costs account for unneglectable latency in wireless communication. GF (Grant-Free) resource allocation [ 38 ] has been proposed by 3GPP (3rd Generation Partnership Project) [ 39 ], which is dedicated to accommodating low latency transmission in both uplink and downlink. In terms of uplink, it is a problem of CG (Configured Grant); with regards to downlink, it is Semi-Persistent Scheduling.…”

Section: Applications and Challenges Of The Envisioned 6g Contextmentioning

confidence: 99%

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

et al. 2023

View full text Add to dashboard Cite

With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions.

show abstract

Section: Applications and Challenges Of The Envisioned 6g Contextmentioning

confidence: 99%

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

et al. 2023

View full text Add to dashboard Cite

show abstract

Section: A Related Work and Existing Surveysmentioning

confidence: 99%

“…Many works have reported the progress made on the integration of RIS in RF and/or optical systems for the development of future wireless communications to provide a plethora of services to the users (see [34]- [36] and references therein). In [34], a contemporary overview of the RIS architecture and deployment strategies in the future 6G was presented. On the latter, the authors particularly focused on the integration of the RIS technology with other 6G-enabling applications such as non-orthogonal multiple access (NOMA), THz/mmWave, unmanned aerial vehicle (UAV), mobile edge computing (MEC) and PLS for the system performance improvement.…”

Section: A Related Work and Existing Surveysmentioning

confidence: 99%

Relay Selection for Improving Physical Layer Security in D2D Underlay Communications

2022

View full text Add to dashboard Cite

This paper investigates the physical layer security of inband underlay Device-to-Device (D2D) communication, where the direct link between D2D users is not available. In this respect, optimal relay and suboptimal relay selections are utilized to secure the D2D transmission. The eavesdropper uses either maximal-ratio combining or selection combining to increase the wiretapped signals. The D2D secrecy performance analysis is performed regarding the secrecy outage probability (SOP) and the probability of non-zero secrecy capacity, and closed-form expressions are provided for both relay selection approaches and verified using Monte-Carlo simulation. From the numerical results, it is shown that increasing the number of D2D relays enhances the secrecy performance of D2D communications. Moreover, the exact asymptotic analysis of the SOP is provided. It turns out that the diversity order of both relay selection approaches is the same.INDEX TERMS Physical layer security, D2D communications, secrecy outage probability, optimal and suboptimal relay selection, maximal-ratio combining, and selection combining., 1

show abstract

“…The IRS can boost the communication performance of the networks with accurate channel characteristics and beamforming information. Many articles in the literature provide an extensive review of the work on IRSs 4–9 . These works describe the dynamics of the working of the IRS, its contributions, enhancements proposed, integration with other technologies, challenges faced and the potential applications of the IRS.…”

Section: Introductionmentioning

confidence: 99%

“…Many articles in the literature provide an extensive review of the work on IRSs. [4][5][6][7][8][9] These works describe the dynamics of the working of the IRS, its contributions, enhancements proposed, integration with other technologies, challenges faced and the potential applications of the IRS. IRS can be integrated into other technologies to achieve and tend to greater applications.…”

Section: Introductionmentioning

confidence: 99%

Beamforming and data detection in intelligent reflecting surfaces‐assisted communication using deep learning

Raghavendra,

Gurugopinath

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

SummaryIn this article, we propose two deep neural network (DNN) architectures for an intelligent reflecting surface (IRS)‐assisted network with a single transmitter and multiple receivers. The first DNN is designed to obtain an optimal beamforming weight vector that maximizes the achievable sum rate over the IRS‐aided network. The beamforming vector is designed based on a pilot‐based channel estimation procedure, which requires only a small fraction of the available number of reflecting surfaces on the IRS to be active. The obtained vector is fed to the second DNN, which is designed for detecting information symbols from the transmitter. Our simulation study shows that the achievable rate obtained using the designed beamforming vector approaches the maximum achievable rate quickly, as the number of active elements increases. The impact of the number of active elements and the beamforming vector on the performance of the detector is also studied, in terms of the bit error rate (BER). We show that the BER performance of the second DNN is close to that of the optimal maximum likelihood detector. Further, we study the effect of the channel estimation errors on the performance of the DNNs. Moreover, we study the effect of the hyperparameters of the architectures on the training time and performance, in terms of root mean squared error and accuracy.

show abstract

IRS-Empowered 6G Networks: Deployment Strategies, Performance Optimization, and Future Research Directions

Cited by 14 publications

References 132 publications

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

Relay Selection for Improving Physical Layer Security in D2D Underlay Communications

Beamforming and data detection in intelligent reflecting surfaces‐assisted communication using deep learning

Contact Info

Product

Resources

About