is estimating the unknown states of cellular BTSs, and is sharing such estimates with navigating receivers. Another framework is to simultaneously estimate the states of the receiver and cellular BTSs in a radio simultaneous localization and mapping (radio SLAM) manner [26–29].
This chapter discusses how cellular signals could be used for PNT by presenting relevant signal models, receiver architectures, PNT sources of error and corresponding models, navigation frameworks, and experimental results. The remainder of this chapter is organized as follows. Section 38.2 gives a brief overview of the evolution of cellular systems. Section 38.3 discusses modeling the clock error dynamics to facilitate estimating the unknown BTSs’ clock error states. Section 38.4 describes two frameworks for navigation in cellular environments. Sections 38.5 and 38.6 discuss how to navigate with cellular code‐division multiple access (CDMA) and LTE signals, respectively. Section 38.7 discusses a timing error that arises in cellular networks: clock bias discrepancy between different sectors of a BTS cell. Section 38.8 highlights the achieved navigation solution improvement upon fusing cellular signals with GNSS signals. Section 38.9 describes how cellular signals could be used to aid an INS.
Throughout this chapter, italic small bold letters (e.g.
38.2 Overview of Cellular Systems
Cellular systems have evolved significantly since the first handheld cell phone was demonstrated by John F. Mitchell and Martin Cooper of Motorola in 1973. The first commercially automated cellular network was launched in Japan by Nippon Telegraph and Telephone (NTT) in 1979. This first generation (1G) was analog and used frequency division multiple access (FDMA). The second generation (2G) transitioned to digital and mostly used time‐division multiple access (TDMA), which later evolved into 2.5G: General Packet Radio Service (GPRS) and 2.75G: Enhanced Data Rates for GSM Evolution (EDGE). The third generation (3G) upgraded 2G networks for faster Internet speed and used CDMA. The fourth generation (4G), commonly referred to as LTE, was introduced to allow for even faster data rates. LTE used orthogonal frequency division multiple access (OFDMA) and featured multiple‐input multiple‐output (MIMO), that is, antenna arrays. Figure 38.1 summarizes the existing cellular generations and their corresponding predominant modulation schemes.
This chapter focuses on using cellular CDMA and LTE signals for PNT. Table 38.1 compares the main characteristics of (i) GPS coarse/acquisition (C/A) code, (ii) CDMA pilot signal, and (ii) three LTE reference signals: primary synchronization signal (PSS), secondary synchronization signal (SSS), and cell‐specific reference signal (CRS).
Figure 38.1 Cellular systems generations.
Source: Adapted from A. Elnashar, “Wireless Broadband Evolution,” http://www.slideshare.net/aelnashar/ayman‐el‐nashar, June 2011, accessed on: June 2019.
Table 38.1 GPS versus cellular CDMA and LTE comparison
| Standard | Signal | Possible number of sequences | Bandwidth (MHz) | Code period (ms) | Expected ranging precision (m)* |
|---|---|---|---|---|---|
| GPS | C/A code | 63 | 1.023 | 1 | 2.93 |
| CDMA | Pilot | 512 | 1.2288 | 26.67 | 2.44 |
| LTE | PSS | 3 | 0.93 | 10 | 3.22 |
| SSS | 168 | 0.93 | 10 | 3.22 | |
| CRS | 504 | up to 20 | 0.067 | 0.15 |
* 1% of chip width
In 2012, the International Telecommunication Union Radiocommunication (ITU‐R) sector started a program to develop an international mobile telecommunication (IMT) system for 2020 and beyond. This program set the stage for 5G research activities. The main goals of 5G compared to 4G include (i) higher density of mobile users; (ii) supporting device‐to‐device, ultra‐reliable, and massive machine communications; (iii) lower latency; and (iv) lower battery consumption. To achieve these goals, millimeter wave bands were added to the current frequency bands for data transmission. Other salient features of 5G include millimeter waves, small cells, massive MIMO, beamforming, and full duplex [30, 31].
38.3 Clock Error Dynamics Modeling
GNSS SVs are equipped with atomic clocks, are synchronized, and their clock errors are transmitted in the navigation message along with the SVs’ orbital elements. In contrast, cellular BTSs are equipped with less stable oscillators (typically OCXOs), are roughly synchronized to GNSS, and their clock error states (bias and drift) and positions are typically unknown. As such, the cellular BTSs’ clock errors and positions must be estimated. Therefore, it is important to model the clock error state dynamics. To this end, a typical model for the dynamics of the clock error states is the so‐called two‐state model, composed of the clock bias δt and clock drift
