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Subjects:
Telecommunications
Future generation communication systems are aiming to provide a tremendous high data rate with low-latency high reliable and three-dimensional coverage. To achieve such a challenging goal, it is required to have very precise location information related to the mobile terminal. The advancement of signal processing techniques and communication technologies enables the path for improving localization performance. Recently, intelligent reflecting surface (IRS) has been widely considered as the key element for the future generation of wireless communication. Over the past few years, the performance of IRS-assisted networks is extensively investigated from the point of view of communication purposes and its improvement. However, by virtue of its potential, IRS finds its application for wireless localization.
- IRS
- Wireless Localization
- Radio Localization and Mapping
1. Introduction
IRS represents a breakthrough technology to fulfill the goals of the future generation communication and localization system by exploiting its capability to actively modify the incident electromagnetic wave [1]. The IRS is basically a collection of IRS units and these units/elements are responsible for changing the behavior of the incident signals by independently controlling the amplitude, phase frequency, and polarization of the incident signals [2,3,4,5]. Through numerous research works it has been already demonstrated that a very directive and high gained beam-formation can significantly improve the signal quality or can be utilized for interference nulling. These benefits can be utilized to extract the wireless localization information with minimum errors. In addition, the IRS provides resounding benefits, such as that it is very cost-effective and low in energy consumption.
The localization of devices is an important aspect to ensure seamless connectivity in a network. Particularly, in sensor networks, localization information is of utmost importance to ensure reliable multi-hop connectivity. Similarly, location information is very important for efficient network planning and dynamic resource allocation in order to enhance link quality. With the growing demand and the advancement in society, its find lots of applications such as the industrial Internet of Things (IIoT), the intelligent transportation system (ITS), simultaneous wireless information and power transfer (SWIPT), location-aware communication system, radar, robot localization and extended reality, etc. [7]. Over the past few years, lots of works have been carried out and several approaches have been proposed to improve the localization accuracy. In this regard, IRS provides an extra degree of freedom to improve the localization information. Such a platform can be used in indoors and outdoors. This motivates the researchers to explore the possibilities of IRS for localization. In [8] the authors have demonstrated that the position error bounds (PEB) and orientation error bounds (OEB) can be significantly improved by utilizing IRS. It also improved the multi-user location by improving the signal strength with the help of IRS [9]. Apart from signal strength-based measurement, time-delay can be exploited as the same is presented in [10]. Here, the authors have optimized the PEB by optimizing the phase distribution for the IRS elements and their selection. Furthermore, the IRS can be utilized as a reflector or lens for sensing and localization purpose. The same is reported, in [11] IRS for assisting sensing applications and in [12,13,14] IRS lens for the localization. The impact of the IRS size, IRS deployment strategies, and related impairments are discussed in [12]. The impact of IRS quantization on the localization performance is reported in [13].
2. Wireless Localization
Wireless Localization System is a system to locate the desired object. This can be achieved using the local area networks such as Wi-fi, cellular area networks, or using GNSS. The nomenclature given to the target object is AgN(AgN) or mobile user whose location is completely unknown and the reference nodes whose locations are known are referred to as anchor nodes (ANs) or landmarks. The wireless localization is used to estimate the position of the AgNs with reference to the ANs and it locates the estimated position on a coordinate of a map where several ANs are placed.
Generally, a localization system build with two major ingredients: (i) a set of ANs [whose location details are known]; (ii) an estimation unit (EU) that can be deployed on the AgN/AN or at some other remote location.
The localization process can be broadly summarized as:
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A reference signal is transmitted from the AN or the AgN and the same is measured at the other end of the link to have certain location-based information such as RSS, AoA/AoD, ToA, and TDoA, etc.
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All the information received at step (1) is used by the local estimation unit (LEU) to approximate the location of the AgN/AN.
Localization systems can be grouped on the basis of location-based algorithms [15] or location infrastructures [16]. However, self-localization and remote localization systems are considered the most popular ones.
Self-localization: The AgN which is embedded with LEU receives reference signals from different ANs. The AgN is efficient enough to perform signal measurement based on which its own location is determined.
Self localization has several advantages. Some of them are mentioned below:
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Since all the localization algorithm depends only on the AgN, the computational efficiency of the AgN will determine the speed of operation. Hence, a small change or update of hardware/software at the AgN may increase the system’s overall performance. No need to change the entire network infrastructure.
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Since all the localization algorithm is implanted at the AgN, the possibility of leaking the information reduces as ANs act as only a transmitter with all authorization access limited to the AgN only.
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Dynamic localization scenarios can be further implanted on the AgNs to provide some motion information so that the accuracy can be improved further [17].
However as mentioned above, the AgN requires high computational efficiency and thus can be deployed only on devices enriched with powerful computational capability.
Remote Localization: The reference signal is transmitted from the AgN to the ANs. Once all the ANs receive the signal, it forwards the same to the central station for location estimation.
The advantage of this system over self-localization is, the pressure on the AgN is reduced and thus all the computational work is done at the central station sometimes referred to as base stations (BS). Therefore, this kind of mechanism is considered more useful for resource-limited devices such as IoT, and sensor nodes.
Unlike self-localization where all the location-based information is stored only at the AgN, in this method the central station stores the location information of several AgNs. The security becomes a major concern as a single central station is processing and storing the location information of several AgNs.
The basic localization technique is broadly classified into direct localization [18] and two-step localization [19]. In direct localization, the received signal is processed for the location estimation of the AgN, whereas the information from, e.g., RSS, ToA, AoA, and TDoA, is extracted at first in case of two-step localization. These information is utilized for the location estimation of the AgN. In terms of efficiency, the direct localization technique is proved better over the two-step localization; however, if system complexity and implantation constraints are considered, then two-step localization is preferred in most of the practical applications. Going further, The two-step localization is categorized as (1) geometric based localization, (2) scene analysis, and (3) proximity approach.
Geometric based localization(GBL): As the name suggests, it uses geometric properties such as trilateration and triangulation of a triangle to estimate the location of the AgN. Trilateration, also called as ranging uses the distance-related information from different ANs to estimate the location of the AgN [20,21,22]. On the contrary, triangulation measures the AoAs of the received signal from different ANs and the AgNs and estimates the location at the intersection of the angle direction lines [23].
Scene Analysis/Fingerprinting-based Localization: The efficiency of GBL reduces to a lower extent in complex environments. Thus, an alternative approach has to be looked out based on scene analysis or fingerprint [24,25,26,27]. Such methods are used to collect data from different sensors such as cameras, wireless apps, etc., and extract specific information such as geotagged signatures (fingerprints), and then estimates the location of the AgNs.
Proximity-Based Localization: This technique works on proximity constraints [28]. It depends on the location of the actual ANs, thus the efficiency relies on the density of the ANs. The efficiency of the technique is directly proportional to the number of ANs. This method is simple to implement, however, as the performance depends on the density of ANs; therefore, it finds its application where the location accuracy can be compromised.
3. IRS-Assisted Radio Localization and Mapping (RLM)
Improved RLM of mobile units (MUs) and other Internet of Things (IoT) devices using IRS is a key ingredient of 6G systems. The coexistence and cooperation between sensing, localization, and communication aims to boost security and trust in 6G connectivity in indoor and outdoor scenarios.
3.1. IRS Asisted Microwave/Millimeter-Wave Localization
As the world is moving towards the 5G and beyond (5GB) communication system, it is evident to have better localization accuracy due to its higher frequencies of operation. At a higher frequency, it is very much likely to have a blockage in the line-of-sight (LoS) path between the transmitter and the receiver. Under such circumstances, it is required to explore the multipath-aided localization.
The intelligent controlling capability of the propagation environment makes IRS an attractive research topic for localization and mapping.
The introduction and advancement of the IRS improve the system performance significantly. This is because of the measurement accuracy of the RSS, ToA, AoD, PoA, AoA, and the Doppler shift defense of the nature of the wave and the channel. The inclusion of the IRS provides an additional degree of freedom. In the case of an IRS-assisted RLM system, the measurement accuracy can be increased significantly by optimizing the power allocation, beam formation etc. [12,30]. As in [31], the authors proposed an alternative optimization method and a GDM-based algorithm to optimize the reflect beamforming in order to estimate the mobile station position more accurately. Along with the passive element reflecting surface, active large intelligent surfaces (LISs) are also exploited to enhance localization. As in [12,32], authors investigated the distributed and centralized LIS systems in terms of Cramer–Rao lower bounds (CRLB) of all the dimensions. The proposed scheme aims to increase the robustness by subdividing the reflecting surface area into smaller units and to increase the coverage to have improved positioning. From the point of view of positioning, RSS plays an important role. With the of improving the RSS for the mobile users, authors in [33], have proposed Spherical LIS systems, which have Lower CRLB compared to planar LIS.
3.2. IRS Asisted THz Localization
For future generation communication systems, Terahertz (THz) communications are the key players for converged localization. It enjoys more precise localization and high angular resolution [29]. As in [43], the authors demonstrated that with identical total transmission power and time, THz-based localization is approximately 5 (20) times more accurate than the mmWave-based localization without (with) prior position information. Although the severe path losses in THz band make the localization and the mapping more difficult. Due to the strong directionality, the THz waves hardly cover the blind areas, and therefore, with the blocked LoS path, the localization problem becomes more challenging.
Lots of works have been carried out in this field but mostly in the microwave and mmWave frequency bands. A thorough analysis of THz localization, related challenges, and possible approaches are discussed in [44,45,46]. As discussed the biggest problems in THz localization are path/penetration losses and lower scattering profile. To get rid of such a problem, the use of coherent array processing and relay networks seems to be not useful. Under such a situation, IRS provides the breakthrough by converting the channel into a favorable environment [44,45].
The size of the IRSs at THz bands and its controlled scattering feature make it the most feasible and effective solution. Moreover, IRSs could enable tracking/surveillance applications in NLoS communications and autonomous localization. As discussed in [46], the localization performance of the SLAM system can be improved by utilizing the high-resolution THz images but at the cost of a complex model. However, the THz localization system enables us not only to have the fine-grained location information but also to enrich us with electromagnetic properties and material types in the target objects.
3.3. IRS-Assisted Airborne Mobile Networks Localization
Unmanned Aerial Vehicles (UAVs) have gained lots of attention from the research community due to their mobility and easily deployment. UAV-assisted communication networks to gain significant importance in scenarios where LoS links are obstructed due to the presence of physical structures. The main motivation behind the UAV-assisted airborne mobile networks is to extend the coverage by avoiding the coverage blind spot.
From the point of view of the key performance matrix energy efficiency (EE) and as influencing fact, the trajectory of the UAVs have a direct impact on the reliability and seamless connectivity of the airborne network [47,48]. Considering the opportunities created with the advancement of IRS, its deployment in airborne platforms is gaining significant attention from researchers around the world. By exploiting the potential of IRS, a combination of IRS with UAV-assisted [49] communication networks can improve the overall network performance in terms of high precision localization, extended coverage, high energy efficiency, security, and low-cost network densification.
This entry is adapted from the peer-reviewed paper 10.3390/electronics11091411
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