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A Comprehensive Review on Beamforming Optimization Techniques for IRS assisted Energy Harvesting

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Abstract

Intelligent reflecting surfaces (IRS) recently gained prominence due to their ability to adapt and tweak their configuration in real-time to create an intelligent wireless environment. Hence, it can elevate wireless connectivity, signal strength, data rate, coverage, and mitigate signal blockage or interference in future wireless networks. A comprehensive review of IRSs has been conveyed in this paper, emphasizing beamforming optimization strategies in the realm of energy harvesting with IRS assistance. The discussion encompasses an overview of IRS hardware design, practical IRS prototypes for hardware design, a summary of related works, and an equivalent RLC circuit model. Additionally, an extensive comparative analysis of IRS architecture, shape, size, advantages, drawbacks, and applications is presented, considering existing research. Further, the paper examines the most pivotal cost and economic aspects of IRS to optimize energy harvesting and coverage enhancement. The paper explores beamforming techniques and examines various optimization methods aimed at maximizing the potential of IRS for energy harvesting. Furthermore, the paper delves into the wide range of potential applications that IRS-assisted wireless communication networks can offer. Despite the significant promises of IRS technology, it faces substantial research challenges in optimization. This paper addresses and highlights these challenges and limitations associated with the IRS, paving the way for future research directions.

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Abbreviations

IRS:

Intelligent reflecting surface

SWIPT:

Simultaneous wireless information and power transfer

IoT:

Internet of things

B5G:

Beyond 5G

Gbps:

Gigabit per second

EE:

Energy efficiency

URLLC:

Ultra-reliable and low-latency communications

E2E:

End-to-end

QoS:

Quality of services

2D:

Two-dimensional

3D:

Three-dimensional

LIMs:

Large intelligent metasurfaces

SDMs:

Software-controlled metasurfaces

PIS:

Passive intelligent surfaces

D2D:

Device-to-device

FC:

Fully-connected

DC:

Dynamically-connected

SDR:

Semi-definite relaxation

JBFO:

Joint beamforming optimization

MM:

Majorization-minimization

PLS:

Physical layer security

UAV:

Unmanned aerial vehicle

V2X:

Vehicle-to-everything

AI:

Artificial intelligence

FPGA:

Field-programmable gate array

AP:

Access point

GHz:

Gigahertz

DC:

Direct current

AmBC:

Ambient backscatter communications

PEC:

Perfect electric conductor

HFSS:

High-frequency structured simulator

BER:

Bit error rate

WIPT:

Wireless information and power transfer

SIW:

Substrate integrated waveguide

PA:

Power amplifier

UIRS:

Ultra-intelligent reflecting surface

SiO\(_{2}\) :

Silicon-dioxide

AIN:

Aluminium nitride

ISI:

Intersymbol interference

OOK:

On-Off keying

SINR:

Signal-to-interference plus noise ratio

RCI:

Regularized channel inversion

UPA:

Uniform planar array

DAC:

Digital-to-analog converter

E-AoA:

Elevation-angle-of-arrival

E-AoD:

Elevation-angle-of-departure

MISO:

Multiple input single output

AWGN:

Additive white Gaussian noise

UM-MIMO:

Ultra massive-multiple input multiple output

HB:

Hybrid beamforming

AoSA:

Array-of-subarray

Mu:

Multiuser

IIoT:

Industrial-internet-of-things

SDs:

Sensor devices

CWPCN:

Cognitive wireless powered communication network

IRSs:

Intelligent reflecting surfaces

5G:

Fifth-generation

mm:

Millimeter

6G:

Sixth-generation

SE:

Spectral efficiency

eMBB:

Enhanced mobile broadband

mMTC:

Massive machine-type communications

THz:

Terahertz

B6G:

Beyond 6G

EM:

Electromagnetic

LIS:

Large intelligent surfaces

SDS:

Software defined surfaces

RIS:

Reconfigurable intelligent surfaces

PIMs:

Passive intelligent mirrors

SNR:

Signal-to-noise ratio

PC:

Partially-connected

PBFO:

Passive beamforming optimization

AO:

Alternating optimization

SCA:

Successive convex approximation

ADMM:

Alternating direction method of multipliers

M2M:

Machine-to-machine

IoUT:

Internet-of-underwater things

MEC:

Mobile edge computing

FSS:

Frequency selective surfaces

BS:

Base station

PCB:

Printed circuit board

DSP:

Digital signal processing

SRR:

Split-ring resonator

EIRP:

Effective isotropic radiated power

UE:

User equipment

RF:

Radio frequency

WPT:

Wireless power transfer

PAA:

Phased antenna array

FR-4:

Flame retardant-4

VO\(_{2}\) :

Vanadium-dioxide

CIRS:

Channel impulse responses

Si:

Silicon

RSSI:

Received signal strength indication

WNoC:

Wireless network on chip

LC:

Liquid crystal

LOS:

Line-of-sight

IBI:

Inter-band interference

A-AoA:

Azimuth-angle-of-arrival

ADC:

Analog-to-digital converter

A-AoD:

Azimuth-angle-of-departure

NLOS:

Non-line-of-sight

CFO:

Carrier frequency offset

MIMO:

Multiple input multiple output

DoF:

Degree-of-freedom

LSAS:

Large-scale antenna system

DAoSA:

Dynamic AoSA

WET:

Wireless energy transfer

WD:

Wireless device

BCD:

Block coordinate descent

DL:

Downlink

UL:

Uplink

WPHN:

Wireless powered heterogeneous network

RMO:

Riemannian manifold optimization

EH:

Energy harvesting

SD\(_{3}\) :

Robust DRL algorithm

QRM:

Quadrature reflection modulation

SISO:

Single-input single-output

MU:

Multi-user

EHDs:

Energy harvesting devices

FD:

Full-duplex

CSI:

Channel state information

SPCA:

Sequential parametric convex approximation

HAP:

Hybrid access point

DC:

Difference-convex

WPSN:

Wireless powered sensor network

IA:

Inner approximation

SAA:

Sample average approximation

WDs:

Wireless devices

MRT:

Maximum ratio transmission

CVX:

Convex programming

EVD:

Eigenvalue decomposition

SOC:

Second-order cone

I-GRASP:

Improved greedy randomize adaptive search procedure

IUs:

Information users

WDT:

Wireless data transfer

ERs:

Energy receivers

CABC:

Cooperative ambient backscatter communications

MAQ-PG:

Multi-agent Q-mix with policy gradient

APIA:

Alternating pragmatic iterative algorithm

RSMA:

Rate-splitting multiple access

BSO:

Block structured optimization

LC-AO:

Low complexity alternating optimization

SOS1:

Special ordered set of type 1

SA:

Successive approximation

WSP:

Weighted sum power

SPA:

Successive polyblock approximation

IUs:

Information users

mMIMO:

Massive-MIMO

LT:

Legitimate transmitter

AUs:

Aerial users

DQN:

Deep Q-network

AUVs:

Autonomous underwater vehicles

V2I:

Vehicle-to-infrastructure

RAIVC:

Resource allocation for IRS-aided vehicular communications

ASC:

Average secrecy capacity

WVN:

Wireless vehiclar network

AFFG:

Amplify-and-forward fixed gain

DC:

Difference-of-convex

DL:

Deep learning

LISA:

Large intelligent surface/antennas

ORA:

Open research area

Tx:

Transmitter

AIRS:

Amplfying IRS

DACs:

Digital-to-analog converters

PTCD:

Planar tightly coupled dipoles

OpEx:

Operating expenditures

LDT:

Lagrangian dual transformation

LEO:

Low-earth orbit

IWE:

Intelligent wireless ecosystem

WIT:

Wireless information transfer

SDP:

Semi-definite programming

BCD:

Block coordinate descent

DRL:

Deep reinforcement learning

IFC:

Interference channel

IPM:

Interior-point method

SU:

Single-user

FF:

Frequency-flat

CRSNs:

Cognitive radio sensor networks

WPCN:

Wireless-powered communication network

AO-INPM:

AO IRS-aided nonlinear harvested power maximization

ID:

Information decoding

NOMA:

Non-orthogonal multiple access

IR:

Information receiver

ES:

Energy source

IDRs:

Information decoding receivers

TMP:

Turbo message passing

QOPs:

Quadratic optimization problems

QCQP:

Quadratically constrained quadratic program

CSCG:

Circularly symmetric complex Gaussian

ABFO:

Active beamforming optimization

SLA:

Successive linear approximation

EUs:

Energy users

STMO:

Successive target migration optimization

SOCP:

Second-order cone program

PS:

Power-splitting

MAQ-WP:

Multi-agent Q-mix with wolpertinger

CR:

Cognitive radio

PS:

Power-splitting

BSUM:

Block successive upper bound maximization/minimization

WSR:

Weighted sum rate

PS:

Power-splitting

RL:

Reformulation linearization

PB:

Power beacon

MO:

Monotonic optimization

Eves:

Eavesdroppers

AN:

Artificial noise

ANN:

Artificial neural network

LR:

Legitimate receiver

FIRS:

Flying IRS

DDPG:

Deep deterministic policy gradient

RSU:

Roadside unit

V2V:

Vehicle-to-vehicle

VANET:

Vehicle ad-hoc network

SOP:

Secrecy outage probability

DF:

Decode-and-forward

CCM:

Complex circle manifold

CE:

Channel estimation

ACK:

Acknowledgement

PUC:

Potential use cases

LDO:

Low-dropout regulator

Rx:

Receiver

ASICs:

Application-specific integrated circuits

LEs:

Loading elements

CaEx:

Capital expenditures

TCO:

Total cost of ownership

PBMO:

Penalty-based manifold optimization

ISAC:

Integrated sensing and communication

IWEs:

Intelligent wireless ecosystems

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Acknowledgements

The authors are pleased to express their gratitude to Sikkim Manipal Institute of Technology, Sikkim Manipal University for their unwavering support and encouragement throughout this research endeavour.

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  1. Pradeep Vishwakarma, Dipanjan Bhattacharjee, Sourav Dhar and Samarendra Nath Sur have contributed equally to this work.

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  1. Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Rangpo, Sikkim, 737136, India

    Pradeep Vishwakarma, Dipanjan Bhattacharjee, Sourav Dhar & Samarendra Nath Sur

  2. Centre of Distance and Online Education (CDOE), Sikkim Manipal University, Majitar, Rangpo, Sikkim, 737136, India

    Sourav Dhar

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Vishwakarma, P., Bhattacharjee, D., Dhar, S. et al. A Comprehensive Review on Beamforming Optimization Techniques for IRS assisted Energy Harvesting. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10118-2

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