Deep Learning for Large Intelligent Surfaces in Millimeter Wave and Massive MIMO Systems

Taha, Abdelrahman; Alrabeiah, Muhammad; Alkhateeb, Ahmed

doi:10.1109/globecom38437.2019.9013256

Cited by 152 publications

(126 citation statements)

References 8 publications

Supporting

Mentioning

126

Contrasting

Order By: Relevance

“…However, with the involvement of RIS, this assumption may no longer be valid, which further complicates the problem [14]. A practical approach for acquiring the CSI at the RIS is proposed in [57], [58], where some of the reflecting elements are assumed to be active and can estimate the CSI, then, using compressive sensing or deep learning techniques, the CSI at all reflecting elements can be recovered/estimated. However, the channel is assumed to be sparse in the compressive sensing technique and requires a higher number of active elements as compared to the deep learning approach.…”

Section: Effect Of Csi Availability and Imperfect Csimentioning

confidence: 99%

Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey

Almohamad

Tahir

Al‐Kababji

et al. 2020

IEEE Open J. Commun. Soc.

110

View full text Add to dashboard Cite

With the emergence of the internet of things (IoT) technology, wireless connectivity should be more ubiquitous than ever. In fact, the availability of wireless connection everywhere comes with security threats that, unfortunately, cannot be handled by conventional cryptographic solutions alone, especially in heterogeneous and decentralized future wireless networks. In general, physical layer security (PLS) helps in bridging this gap by taking advantage of the fading propagation channel. Moreover, the adoption of reconfigurable intelligent surfaces (RIS) in wireless networks makes the PLS techniques more efficient by involving the channel into the design loop. In this paper, we conduct a comprehensive literature review on the RIS-assisted PLS for future wireless communications. We start by introducing the basic concepts of RISs and their different applications in wireless communication networks and the most common PLS performance metrics. Then, we focus on the review and classification of RIS-assisted PLS applications, exhibiting multiple scenarios, system models, objectives, and methodologies. In fact, most of the works in this field formulate an optimization problem to maximize the secrecy rate (SR) or secrecy capacity (SC) at a legitimate user by jointly optimizing the beamformer at the transmitter and the RIS's coefficients, while the differences are in the adopted methodology to optimally/sub-optimally approach the solution. We finalize this survey by presenting some insightful recommendations and suggesting open problems for future research extensions.

show abstract

Section: Effect Of Csi Availability and Imperfect Csimentioning

confidence: 99%

Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey

Almohamad

Tahir

Al‐Kababji

et al. 2020

IEEE Open J. Commun. Soc.

110

View full text Add to dashboard Cite

show abstract

“…Taha et al [19] propose a training process based on deep learning. Using a massive number of passive reflectors and a small number of active ones, the LIS can learn the channel parameters and autonomously optimize the data transmission.…”

Section: Introductionmentioning

confidence: 99%

Bit Error Probability for Large Intelligent Surfaces Under Double-Nakagami Fading Channels

Ferreira

Facina

Figueiredo

et al. 2020

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

In this work, we investigate the probability distribution function of the channel fading between a base station, an array of intelligent reflecting elements, known as large intelligent surfaces (LIS), and a single-antenna user. We assume that both fading channels, i.e., the channel between the base station and the LIS, and the channel between the LIS and the single user are Nakagami-m distributed. Additionally, we derive the exact bit error probability considering quadrature amplitude (M-QAM) and binary phase-shift keying (BPSK) modulations when the number of LIS elements, n, is equal to 2 and 3. We assume that the LIS can perform phase adjustment, but there is a residual phase error modeled by a Von Mises distribution. Based on the central limit theorem, and considering a large number of reflecting elements, we also present an accurate approximation and upper bounds for the bit error rate. Through several Monte Carlo simulations, we demonstrate that all derived expressions perfectly match the simulated results. INDEX TERMS Bit error rate, large intelligent surfaces, massive MIMO, Nakagami-m fading, von Mises circular distribution.

show abstract

“…Having discussed both EE and power consumption from an optimization perspective, we hereby review several optimization formulations of maximizing the data rate gains to fully exploit the IRS technology. In [51], all the IRS' elements are considered passive in the presence of a few active elements, which are controlled by the IRS controller. The IRS discover the best way to interact with the incoming signal, provided the active elements, by using a deep learning-based solution.…”

Section: Sum-rate Maximizationmentioning

confidence: 99%

“…A comprehensive search is required to look for the optimal beamforming reflected vector w * , which does not have a closed-form solution because of the quantized codebook constraint and the time-domain exertion of the beamforming vector [51]. This extensive search increases the complexity of hardware implementation and power consumption significantly.…”

Section: Sum-rate Maximizationmentioning

confidence: 99%

“…For that reason, the objective is to design IRSaided systems based on a deep learning solution to find the optimal achievable rate while satisfying a low-training overhead and low hardware complexity. To this end, the work in [51] proposes a deep learning-based solution to predict the optimal reflection matrix while satisfying a low training overhead. The authors have also proposed two other methods for the IRS system design with unknown channel knowledge; one is based on compressive sensing [52], and the other is based on deep reinforcement learning [53].…”

Section: Sum-rate Maximizationmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques

et al. 2020

View full text Add to dashboard Cite

This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support the sixth-generation wireless physical platforms (6G). Due to their ability to adjust the behavior of interacting electromagnetic (EM) waves through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a lowcost, green, sustainable, and energy-efficient solution for 6G systems. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid radio frequency/visible light communication (RF-VLC) systems, health considerations, and localization. INDEX TERMS 6G technology, large intelligent surfaces (LIS), massive multiple-input multiple-output (mMIMO), millimetre waves (mmWave) communication, wireless communication.

show abstract

Deep Learning for Large Intelligent Surfaces in Millimeter Wave and Massive MIMO Systems

Cited by 152 publications

References 8 publications

Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey

Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey

Bit Error Probability for Large Intelligent Surfaces Under Double-Nakagami Fading Channels

Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques

Contact Info

Product

Resources

About