# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial

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## Abstract

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## 1. Introduction

#### 1.1. Motivation

#### 1.2. Massive MIMO, Ultra Massive MIMO, and Cell-Free Massive MIMO

#### 1.3. Organization of This Paper

## 2. Several Promising Techniques: IRS, LIS, and Radio Stripes

#### 2.1. Categorizing Recent Studies on Intelligent Reflecting Surfaces

#### 2.1.1. Capacity and Data Rate Evaluations of IRS-Aided Communications

#### 2.1.2. Power/Spectral Optimizations in IRS-Aided Communications

#### 2.1.3. Channel Estimation for IRS-Aided Communications

#### 2.2. Categorizing Recent Studies on Large Intelligent Surfaces

#### 2.2.1. Power Consumption LIS-Aided Communications

#### 2.2.2. LIS-Aided Communications with Decreased User Interference

#### 2.2.3. Complexity Analysis of LIS-Aided Communications

#### 2.2.4. Capacity/Data Rate Analyses of LIS-Aided Communications

#### 2.3. Categorizing Recent Studies on Radio Stripes

#### 2.3.1. Radio Stripes Are Inexpensive

#### 2.3.2. Simple Implementation

_{C}> 30 GHz) in radio stripe networks allows for very compact antenna elements, making the mmWave band ideal for the purpose of providing exceptionally fast data rates (1–5 Gb/s) and ubiquitous connection. RS can be used in virtually any place. RS can be placed inside metros, buses, and populated streets [14].

#### 2.3.3. Radio-Stripe Network Propagation

#### 2.3.4. Radio Stripe Network Path Loss

_{C}> 100 GHz). The omnidirectional free space path loss increases with frequency according to Friis law (the power received is equal to the square of the wavelength).

#### 2.3.5. Radio Stripe Network Energy Efficiency

## 3. System and Signal Characterization

- Linear feedforward, non-iterative FDE receivers (this type includes the ZF, MMSE, MRC, and EGC).
- Iterative MRC and EGC, FDE receivers, known as iterative block-decision feedback equalization (IB-DFE) receivers.

#### System Model and Receiver Design of Receivers

## 4. Conclusions

## 5. Future Research

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Block diagram of a typical scenario of radio stripe deployment [14].

Reference | Main Contribution |
---|---|

[5] | Monte Carlo simulations demonstrated that capacity degradation due to phase errors is inversely proportional to SNR, which is more apparent for large L values. |

[15] | An energy-efficient design is created to maximize the system’s energy efficiency while considering both transmit power and IRS phase shift limits. |

[19] | Because of the uncertainties imposed by environment dynamics and the quick changes in the IRS setup, channel estimate is a vital task of IRS. This research provides a FL framework for simultaneously estimating direct and cascaded channels in IRS-assisted wireless systems. |

[21] | This paper aims to characterize the fundamental capacity limit of IRS-aided point-to-point MIMO communication systems with multi-antenna transmitters and receivers. They examined how best to optimize the IRS reflection coefficients and the MIMO transmit covariance matrix. |

[22] | This article evaluates the IRS’s capacity limits. It investigates ways to jointly optimize the IRS reflection matrix and wireless resource allocation while limiting the number of IRS reconfiguration times. |

[23] | The energy efficiency of the network is maximized in this research by dynamically regulating the on-off status of each RIS and maximizing the reflection coefficients matrix of the RIS. |

[24] | This paper presented techniques to minimize the UAV energy consumption by IRS. |

[25] | Channel estimation (CE) is somewhat challenging. To solve this problem, this paper designs a CE scheme for large IRS-assisted multi-user wireless communication systems. |

Reference | Main Contribution |
---|---|

[6] | One of the disadvantages of the implementation of the LIS is the complexity of the panels. This paper offers a method for omitting the complexity involved in managing the set of activated panels. |

[7] | It is demonstrated that when terminal density grows, it is preferable to use smaller panels and, as a result, more outputs per m^{2}. |

[8] | This research investigates the capabilities of single-antenna terminals being connected with huge antenna arrays installed on surfaces. That is, the entire surface is used as an IRS array. If the surface area is high enough, the received signal after matched filtering (MF) can be well represented by the intersymbol interference (ISI) channel. |

[16] | The best user assignments can be efficiently obtained using classical linear assignment problems (LAPs) developed based on the pleasant property of effective inter-user interference suppression of the LIS units. |

[27] | The capacity and utility of the surface area are both reduced with HWI due to the greater effective noise level induced by the HWI. A distributed LIS system can be implemented by dividing it into numerous small LIS units, where the effects of the HWI can be considerably reduced due to the smaller surface area of each unit. |

[30] | As the number of antennas increases, hardware impairments, noise, and interference from channel estimate errors and the non-line-of-sight become insignificant. This paper investigated the uplink rate in the presence of restrictions such as device-specific, spatially correlated Rician fading. |

[32] | Coverage and positioning are discussed in this paper. |

[33] | This paper designs a Channel estimation scheme for large LIR-assisted multi-user wireless communication systems. |

[34] | This study discusses the implementation problems associated with the interconnection data rate in the LIS. It additionally examined the system capacity and implementation cost with various design parameters and provided design suggestions for LIS installation. |

Reference | Main Contribution |
---|---|

[9] | This research examines an uplink power allocation strategy aimed at improving network spectral efficiency (SE), which is described as an optimization-constrained issue explicitly considering the max-min fairness situation. |

[14] | This paper present advantages of using radio stripes in mm waves. |

[37] | This article demonstrates how inexpensive it is to implement and operate cell-free radio stripes. |

[38] | This paper evaluates energy consumption of radio stripes with ideal CSI. |

[39] | This approach suppresses interference in cell-free mMIMO while minimizing the cost and front-haul requirements. |

IRS | LIS | Radio Stripes | |
---|---|---|---|

Structure | Massive-MIMO | Beyond Massive-MIMO | Cell-free Massive MIMO |

Antenna type | Passive | Active | Active/Passive |

Deployment | Easy | Easy | Easy |

Capacity/data rate | High | High | High |

Energy efficiency | Good | Slightly high | - |

Channel estimate | Critical | Solved | - |

Propagation | Near-field | Near-field | Near-field |

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**MDPI and ACS Style**

Gashtasbi, A.; da Silva, M.M.; Dinis, R. IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial. *Appl. Sci.* **2022**, *12*, 12696.
https://doi.org/10.3390/app122412696

**AMA Style**

Gashtasbi A, da Silva MM, Dinis R. IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial. *Applied Sciences*. 2022; 12(24):12696.
https://doi.org/10.3390/app122412696

**Chicago/Turabian Style**

Gashtasbi, Ali, Mário Marques da Silva, and Rui Dinis. 2022. "IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial" *Applied Sciences* 12, no. 24: 12696.
https://doi.org/10.3390/app122412696