Elsevier

Ad Hoc Networks

Volume 111, 1 February 2021, 102353
Ad Hoc Networks

An overview of massive MIMO localization techniques in wireless cellular networks: Recent advances and outlook

https://doi.org/10.1016/j.adhoc.2020.102353Get rights and content

Abstract

The massive multiple-input-multiple-output (mMIMO) antenna systems are well known for their capability to achieve high spectral efficiency in wireless communication systems thanks to millimeter waves (mmWaves) which allow a large number of antennas to be deployed at the base stations (BSs). Aside from communication-based services, mMIMO BSs are presently being exploited for location estimation of user equipment due to their high angular resolution, low-cost implementation, and excellent performance in the indoor and clutter urban environments where line-of-sight may not be available. Although various mMIMO localization solutions have been proposed, there are still pressing issues yet to be resolved. To this end, this article first provides an overview of recent and relevant state-of-the-art survey papers on localization. Further to this, we provide various foundational background concepts based on the existing localization techniques applicable to mMIMO localization systems. Furthermore, we discuss various methods under each technique and we also identify some critical factors to be considered in a practical radio environment. Based on these techniques, we provide a comprehensive review of recent works on mMIMO localization. Finally, we suggest key research directions to be addressed in the future and also we discuss key enabling technologies that will enhance the performance of localization systems in 6G communication networks.

Introduction

The field of wireless cellular networks has witnessed a tremendous change in recent years due to the continuous growth in demand for coverage and higher data rate. Two major approaches currently employed to address these problems include the deployment of small-cell base stations to complement the existing macro base stations, and the implementation of a large-scale array of antennas also known as massive multiple-input-multiple-output (mMIMO), at these Base Stations (BSs)[1], [2], [3], [4]. In a wireless communication system, mMIMO enhances the capability of the system to combat fast fading, interference, and noise power thus improving the spectral efficiency and energy efficiency of the system [5], [6], [7]. Interestingly, recent studies show that apart from communication-based services, large-scale antenna arrays can also be exploited for location-based services where the mMIMO BSs can precisely evaluate the position of user equipment (UE) within the network (a technique referred to as UE localization) due to their high angular resolution feature [8,9]. From another perspective, the mmWaves, being a major enabler of mMIMO[10],offer wider bandwidth and have the capacity of improving the accuracy of the localization system [11]. For instance, the raw distance resolution of a 20 MHz bandwidth in the 4G long term evolution (LTE) systems and the 4 GHz bandwidth in the mmWave-mMIMO systems is 15 m and 7.5 cm, respectively [12]. Hence, these attributes, coupled with low-cost implementation make the mMIMO localization approach more effective and preferable than the global navigation satellite system (GNSS) localization methods especially in the indoor environment and dense urban areas where their signals are prone to severe attenuation due to the presence of physical obstacles [13,14]. Moreover, another shortfall of GNSS localization systems is that the signal processing incurs a high computational load which increases the energy consumption of mobile devices and may drain their batteries within few hours [15,16]. Consequently, the GNSS-based localization approach may not be sustainable for tracking devices having small battery capacity. An illustration of UE localization in wireless cellular networks is depicted in Fig. 1.

By exploiting the benefits associated with mMIMO localization services, many economic sectors have been able to expand their networks and improve on their service delivery strategies which translates to a higher profit margin on the part of network operators. The known areas of application of these services include retail and assisted living, intelligent transportation systems, aerial vehicle surveillance systems, and various industrial applications [17]. It is also noteworthy that other than position estimation, mMIMO localization techniques have been found useful for enhancing various network optimization processes such as position-aided beamforming [18,19], robust handover process [20,21], less complex channel state information (CSI) acquisition [22], pilot contamination avoidance [23,24], and resource allocation [25,26].

Various studies on mMIMO localization solutions have been presented in the literature. While improving the localization accuracy with reduced computational complexities has been a major target objective in most designs, some studies have further emphasized on the realization of these solutions under practical conditions. These conditions include situations where the line of sight (LoS) paths may not be available, the effect of near-field and far-field signal sources, and hardware complexities. Hence, with the potential benefits of mMIMO localization highlighted, it is necessary to take cognizance of appropriate models and practical considerations from the design perspective to the implementation aspect. Table 1 presents the list of acronyms used throughout this review.

Notable recent survey articles relevant to localization in wireless cellular networks can be found in [17,[27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45]] and a summary of these surveys are presented in Table 2. In [27], the authors provide a comprehensive review of indoor localization from a technical and analytical perspective. Moreover, the analysis is centered on the sources of localization errors. Further to this, a multi-layered framework comprising of the wireless module unit, signal processing unit, and the network node layer is proposed to address this problem. The article [28] provides an overview of fingerprinting localization techniques. Four broad classifications of these techniques are identified based on the physical characteristics of the user and the propagating signals. Furthermore, the article discusses the basic localization processes involved in the fingerprinting technique. In the latter part of the survey, the performance evaluations of these techniques are presented in order to provide insight to the trade-off of each technique. In [29], the authors present a detailed localization survey under two classifications; the device-based and device-free scenario. In the former case, the localization hardware is attached to the user whereas the user is free of localization hardware in the latter case. Various performance metrics and enabling technologies under each classification are also discussed. A concise review of Wi-Fi-based indoor localization, its applications, and challenges in the internet-of-things (IoT) networks is presented in [30]. Moreover, the article also presents a comparative summary of the identified techniques by exploiting their pros and cons. In the article [31], a comprehensive discussion of various localization technologies and methods is presented. Also presented are different learning algorithms to simplify the complexities associated with localization problems. The challenges and applications of the identified technologies are discussed in the latter part of the article.A survey on cooperative localization is presented in [32] with an emphasis on the device-to-device (D2D) communication system as the key localization technology. A brief discussion of key 5G supporting technologies together with the impact of localization in network optimization is also presented. The authors later formulated a cooperative localization problem with a major goal of minimizing the localization error. The article [33] providesa detailed discussion on the potential benefits of cellular technologies in localization system design. The survey is initiated by a comprehensive review of 2G – 4G cellular technology evolutions. Moreover, different localization system architectures based on each cellular standard are presented. Furthermore, a review of performance of hybrid technique based on the combination of GNSS and cellular approach is presented. The article [34] provides an overview of the indoor positioning system (IPS) with an emphasis on solutions developed for emergency responders. The focus of the review includes discussion on IPS technologies suitable for emergency response operations, the limitations of the technologies, and key requirements of IPS solutions for emergency responders.From another perspective, authors in [35] provide a thorough classification of various technologies for different localization techniques. Further to this, a concise explanation of these technologies and techniques is discussed. Also explained are performance metrics used to critically analyze the identified literature.The contribution in the article [36] is initiated with the benefits of location-based service and identification of different areas of application. Further to this, a review of localization architectures of different cellular generations coupled with their performances is presented. Also provided are challenges encountered with various localization techniques in the radio environment. In [37], the authors provide a comprehensive survey on three major localization technologies viz; cellular networks, wireless local area networks (WLAN), and wireless sensor networks (WSN). Moreover, the article provides a concise description of different localization architectures and techniques. Also, the article emphasizes the importance of user mobility as a major factor to be considered in practical localization system design. Furthermore, works on vertical positioning are evaluated with more emphasis on solutions capable of providing three dimensional (3D) information. The review of indoor mapping solutions based on simultaneous localization and mapping using smartphone devices is presented in the latter part of the article.Localization in WSN is presented in [38]. The aspect of algorithm implementation is first discussed with emphasis on centralized and distributed approaches. Further to this, different localization techniques and technologies are presented. The identified technologies are presented under a device-based and device-free scenario in order to characterize their areas of application. In the latter part of the article, a thorough review of application of the identified technologies in the IoT networks, and the performance evaluation criteria are provided. In another similar article [39], a concise review of localization techniques applicable to radio environment mapping is presented. Moreover, these techniques are discussed under two key technologies viz; cellular networks and GNSS. The performance evaluations of the technologies and techniques are provided in the latter part of the article. Although it is concluded that the GNSS could offer accurate mapping but at the cost of high energy consumption.In the article [17], different areas of applications of localization technologies are presented. The technical challenges of localization technologies in 5G and IoT networks such as multipath fading, computational burden, and hardware complexity are also discussed. The operation of different localization testbeds such as GNSS, mMIMO, ultra-wideband (UWB), and radio frequency identification (RFID) are also presented in the latter part of the article.A comprehensive survey on the potential benefit of localization technologies for autonomous vehicle design is presented in [40]. The key areas addressed in the article include relevant mapping techniques, localization technologies, and cooperative localization.The benefits and limitations of these approaches are highlighted in the concluding remarks. The article [41], provides a detailed classification of various localization techniques and technologies applicable to IPS systems. Moreover, a comprehensive review of works based on the classifications is presented. Furthermore, different areas of applications of the identified technologies and major technical challenges of indoor localization systems are discussed in the latter part of the article. The review provided in [42] focuses on the application of different technologies in IoT networks. Also provided are system constraints that could limit the performance of the identified technologies. Moreover, key metrics to evaluate the performance of the localization solutions are discussed in the latter part of the article. The contribution in the article [43] is centered on the critical analysis of multidimensional scaling of higher dimensional data to smaller dimensions in order to simplify analytical complexities. The authors in [44] provide a concise survey of 5G mMIMO localization technology. The discussion is initiated by channel modeling and parameter estimation of the centimeter waves which is later extended to the mmWaves. Moreover, a brief discussion of various localization techniques for channel modeling and signal propagation conditions is provided. These conditions include the LoS and NLoS situations, cooperative and non-cooperative approach, and direct and indirect localization signal processing. The latter part of the article provides key technical challenges and suggestions for future works. In[45], a comprehensive review of unified location-based and communication-based technologies dubbed integrated localization and communication is presented. The goal is to provide insight into improving 5G and 6G unified localization and communication systems. In the article, the discussion is initiated through various areas of application of localization. Further to this, key localization terminologies and techniques are explained. Moreover, the article emphasizes critical factors and challenges in the infrastructural design of the unified system. Application of UAVs in the aerial-ground systems and performance analysis of the unified systems are provided in the latter part of the article. The discussion is concluded by a comprehensive discussion on future trends and research directions of the unified system.

From the summary of the related survey articles presented in Table 2, it is obvious that mMIMO application in cellular network localization systems is yet to receive a considerable amount of reports except for the ones provided in articles [17,36,37,39,44,45]. Moreover, these articles provide a generalized and non-technical view of mMIMO along with other localization technologies except for the article [44] which is exclusively dedicated to mMIMO in 5G networks. However, a major limitation in the article [44] is that the papers presented are not critically analyzed hence making it difficult to capture their achievements. It is also noteworthy that a significant number of these survey articles are dedicated to indoor positioning systems. This could be attributed to the fact that approximately 80% of the total data demands are generated by indoor users [46,47]. However, regardless of the environment whether indoor or outdoor, the major goal of this work lies in the study of applications and performance of mMIMO technology for localization in wireless cellular networks. The contributions of this survey article are summarized as follows:

  • We provide detailed background concepts of localization techniques and enabling methods that can be used in mMIMO-based cellular networks. These concepts are further supported by illustrative figures and a comparative table. Moreover, we also provide insight into key factors to be considered for the realization of localization systems in a practical radio environment.

  • We review recent literature on mMIMO applications in cellular localization system design. The review is carried out under two broad categories; the geometric-based approach and data-driven approach. Moreover, we also identify the contributions and limitations of each article.

  • We suggest future research directions based on challenges and limitations identified in the reviewed articles and also, we discuss key enabling technologies that will boost the performance of localization systems in the emerging 6G communication networks.

The structure of the paper is presented as follows: In Section 2, we provide the foundational concepts needed to understand the in-depth of mMIMO localization technique. These include the classification of localization techniques and the identification of potential methods applicable to mMIMO technology. We also provide a concise comparative summary of the identified methods in terms of their advantages and disadvantages in order to have an insight into the trade-offs of each technique. The overview of recent works based on these techniques is presented in Section 3. Research directions are delineated in Section 4. In Section 5, we present key enabling technologies that can improve the performance of localization systems in 6G communication networks. Finally, conclusions are drawn in Section 6.

Section snippets

Classification of Localization Techniques

In the open literature, several categories have been identified on localization techniques in wireless cellular networks as indicated in Table 2. In the context of mMIMO, these techniques can be summarized under the three classes as given below and the illustration of these techniques is depicted in Fig. 2. In this work, it is assumed that the BS and UE represent the transmitter and receiver, respectively, and are used interchangeably.

  • 1)

    Triangulation

  • 2)

    Fingerprinting

  • 3)

    Hybrid

  • 1)

    Triangulation: In this

Current Literature

Recent contributions on mMIMO localization can be generally discussed under the geometric-based model and data-driven model. The geometry-based model addresses the localization problem through the estimation of distance and angle between the BS and the target UE. These methods include the triangulation methods, that is, the ToA, RSS, and AoA. The data-driven approach can be referred to as the fingerprinting approach. It relies on creating a database for storing raw channel parameters such as

Research Directions

It has become a reality that mMIMO systems can offer accurate location estimation of UEs in wireless cellular networks. Although several investigations on mMIMO localization solutions have been carried out, there are still challenges yet to be resolved. These challenges include the use of appropriate field models for different signal sources, computational complexity, cooperative localization, improvement in fingerprinting techniques, hardware solution, and algorithm implementation. These

Localization Perspectives: Towards 6G Communication Networks and Beyond

With the proliferation of massive data-intensive applications such as virtual reality and holographic projection, coupled with the surge in the internet of everything (IoE) applications, the gigabits-per-second (Gb/s) peak data rate offer by 5G technologies may become insufficient to support these applications by the year 2030 [175]. Based on this speculation, researchers from academia and industry have started looking into the development of 6G technologies which are expected to offer

Conclusions

The recent advancements in the field of wireless cellular networks have given rise to a lot of new services offered by network operators. In this survey, we focus on location-based services by presenting an overview of recent works on mMIMO localization techniques. These techniques are examined under two broad classifications; the geometric-based and data-driven methods with their critical contributions and limitations identified. Further to this, various design perspectives for the practical

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

The authors would like to thank the anonymous reviewers andthe editor for their valuable comments and constructive suggestions to improve the quality of the paper.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Olumide Alamu received B.Tech (Hons) Degree in Electronic and Electrical Engineering from Ladoke Akintola University of Technology, Ogbomoso, Nigeria, in 2009. He received M.Sc. Degree in Electrical and Electronics Engineering (Communications option) from University of Lagos, Nigeria, in 2016 and currently on Ph.D. programme in the Department of Electrical and Electronics Engineering, University of Lagos, Nigeria. He joined the Department of Electrical and Electronics Engineering, University of

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    Olumide Alamu received B.Tech (Hons) Degree in Electronic and Electrical Engineering from Ladoke Akintola University of Technology, Ogbomoso, Nigeria, in 2009. He received M.Sc. Degree in Electrical and Electronics Engineering (Communications option) from University of Lagos, Nigeria, in 2016 and currently on Ph.D. programme in the Department of Electrical and Electronics Engineering, University of Lagos, Nigeria. He joined the Department of Electrical and Electronics Engineering, University of Lagos, Nigeria, as a lecturer in 2017. He is a registered Engineer with the Council for the regulation of Engineering in Nigeria (COREN) and also a Corporate Member of Nigerian Society of Engineers (NSE). His research interests include wireless cellular heterogeneous networks, self-organizing networks, and green communication networks.

    Babatunde Iyaomolere received B.Sc. Degree in Electrical and Electronics Engineering with First Class Honours from Olabisi Onabanjo University, Nigeria, in 2009. He completed his M.Sc. program in Electrical and Electronics Engineering (Communication option) from University of Lagos, Nigeria, in 2016. Presently, he is a doctorate student at the Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, Nigeria. He is a registered Engineer with the Council for the regulation of Engineering in Nigeria (COREN) and also a Corporate Member of Nigerian Society of Engineers (NSE). His research interests are in Microcontrollers, Signal Processing, Radio Wave Propagation, and mmWave networks. He is currently a lecturer at the Department of Electrical and Electronics Engineering, Ondo State University of Science and Technology, Nigeria.

    Abdulfatai Abdulrahman graduated from the Department of Electrical and Electronics Engineering, University of Lagos, Nigeria, in 2020 where he received B.Sc (Hons) Degree in Computer Engineering. His research interest lies in applications of machine learning in wireless cellular networks.

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