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1. Introduction

Throughout the past several decades, GNSS has become one of the most significant technologies in modern engineering, supporting transportation, communications, finance, emergency response, and critical infrastructure [1]. Its precision, global reach, and reliability have enabled entire industries to scale in ways that would otherwise have been impossible. Yet as GNSS is used more deeply in autonomy-driven and safety-critical domains, the limitations of relying on a single-layer PNT architecture are becoming increasingly apparent.

Urban canyons degrade satellite geometry and tracking performance; intentional and unintentional interference is now commonplace [2]; spoofing has shifted from a theoretical concern to an operational reality; and indoor environments, which are essential for robotics, logistics, and emergency services, remain largely outside GNSS’s physical reach. These challenges are not shortcomings of GNSS itself. They reflect what the system was originally designed to provide: a globally available positioning and timing reference, not the entire resilience burden for every PNT-dependent application.

In parallel, communications technologies have undergone rapid transformation. The evolution from LTE to 5G, and soon to 6G, has introduced wider bandwidths, massive MIMO antenna arrays, improved network synchronization, and dense deployment across urban and indoor environments [3]. At the same time, LEO broadband constellations have matured into powerful satellite infrastructures capable of delivering strong signals, rapid Doppler dynamics, and frequent visibility. Although these systems were built primarily for data connectivity, their physical characteristics naturally lend themselves to positioning and timing.

Taken together, these developments point toward a new direction for resilient PNT: a multi-layer architecture in which GNSS serves as the global reference layer and is complemented by high-power, high-dynamics LEO satellites, terrestrial 5G/6G networks and Wi-Fi systems, and a suite of onboard sensors that provide short-term stability and dead-reckoning capability. Figure 1 illustrates this emerging architecture and highlights how each layer contributes specific observables, coverage strengths, and levels of robustness. The remainder of this article examines the physical foundations of communications-based PNT, the role of LEO as an augmentation space segment, the engineering challenges inherent in multi-source navigation, and the system-level architecture that is now taking shape to deliver resilient and ubiquitous PNT.

2. Rationale Behind Communications–PNT Integration

2. 1 Growing Dependence on PNT and GNSS Vulnerability

Nearly every sector of modern life depends on GNSS-based positioning and timing. As reliance grows, exposure to GNSS limitations grows with it. Dense urban environments create severe multipath and signal blockage; jamming and spoofing incidents are now regularly reported near conflict zones and busy ports [4]; and autonomy concepts in aviation and ground mobility increasingly assume reliable PNT even when GNSS performance is degraded or unavailable.

GNSS will remain the global reference layer, but it was never intended to carry the full burden of these mission-critical demands on its own. A complementary set of technologies is needed, systems that continue to function in GNSS-challenged environments and provide redundancy when satellite signals are unavailable, corrupted, or intermittent.

Error! Reference source not found. illustrates this challenge in a representative urban-canyon environment. Tall buildings restrict line-of-sight to GNSS satellites and generate strong multipath reflections, resulting in weak and unreliable signals (Figure 2a). By contrast, terrestrial networks such as 5G/6G and Wi-Fi maintain strong signal levels and robust geometry because their transmitters are embedded within the built environment, often only tens or hundreds of meters away (Figure 2b). This complementary coverage is a fundamental motivation for integrating communications signals into future PNT architectures.

2.2 Communication Networks Have Quietly Become PNT-Capable

Modern communication networks have evolved far beyond their original purpose of data transport [5]. Several physical-layer characteristics now make 5G, Wi-Fi 7, and future 6G systems surprisingly well suited to PNT:

• Wideband signals. Wi-Fi 7 supports 320-MHz channels and 5G FR2 offers up to 400 MHz, with multi-GHz bandwidths anticipated for 6G [6]. Wider bandwidth directly improves time-of-arrival (ToA) precision. The ToA uncertainty can be approximated by:

• Massive MIMO. Multi-element antenna arrays estimate angle-of-arrival (AoA) and angle-of-departure (AoD), effectively turning base stations into spatial sensors capable of separating line-of-sight from multipath.
• Dense deployment. Unlike GNSS satellites, orbiting at roughly 20,000 km, terrestrial networks are woven directly into the environment. Small cells and access points provide excellent geometry in exactly the locations where GNSS performance is weakest, including city centers, campuses, factories, and warehouses.
• High signal power. Terrestrial signals arrive at the receiver tens of decibels stronger than GNSS, improving indoor penetration, acquisition speed, and robustness to interference.

These features were introduced to enhance connectivity, yet they collectively create an RF landscape that is inherently PNT-capable.

2.3 The Rise of LEO Constellations as a Complementary Space Layer

A third major driver behind communications-enabled PNT is the rapid proliferation of LEO satellite constellations. Broadband systems such as Starlink and OneWeb, together with several emerging PNT-dedicated LEO constellations, offer distinct advantages [7]:

• Stronger received power. LEO satellites operate at altitudes of roughly 500–1,200 km, far closer than GNSS satellites at 20,000 km or higher, resulting in significantly stronger received signals.
• Rapid Doppler dynamics. The relative motion of LEO satellites produces large, fast-varying Doppler shifts, which improve observability of user velocity and, over short intervals, position.
• Large constellation sizes. Hundreds or thousands of satellites create rich geometry and frequent visibility, enhancing availability and resilience.

Although many LEO systems were designed primarily for communications, their signals can already be exploited opportunistically for positioning and timing. Purpose-built LEO-PNT systems extend these capabilities by offering wideband navigation signals, multi-frequency operation, and security features intended specifically for resilient PNT [7].

These characteristics make LEO a natural augmentation layer, strengthening GNSS performance and providing additional robustness in degraded, obstructed, or contested environments.

3. Technical Foundations of Communications-Based PNT

Modern communication and LEO satellite systems provide a diverse set of physical-layer measurements that can be fused with GNSS to create a resilient, multi-layer PNT solution. These observables go well beyond traditional GNSS code and carrier measurements and include Doppler, ranging, time-of-arrival, round-trip time, angle-of-arrival, angle-of-departure, and received signal strength. Figure 3 summarizes this heterogeneous measurement landscape and shows how each layer contributes distinct observables to the fusion engine.

3.1 High-Resolution Ranging from Wideband Waveforms

Ranging accuracy is fundamentally linked to signal bandwidth. GNSS signals typically occupy 1–20 MHz, whereas modern communication waveforms may span hundreds of megahertz. Wider bandwidth enables finer temporal resolution, allowing receivers to separate closely spaced multipath components and improve time-of-arrival (ToA) precision [6].

In practice, Wi-Fi 7 and 5G FR2 waveforms can support sub-meter ranging in favorable conditions and substantially enhance relative positioning indoors and in dense urban environments. Techniques such as two-way ranging, cooperative localization, and inertial smoothing can extend performance even further. As shown in Error! Reference source not found., these wideband ToA and RTT observables form an essential input to the PNT measurement fusion layer.

3.2 Spatial Sensing with Massive MIMO

Massive MIMO arrays are one of the most powerful enablers of communications-based PNT. By comparing the phase and amplitude across many antenna elements, base stations estimate angles of arrival (AoA) and departure (AoD), turning terrestrial infrastructure into distributed RF sensor arrays [8].

Angle-based measurements offer several important benefits:

• Improved localization geometry in 3D urban canyons
• Ability to distinguish line-of-sight (LOS) from multipath
• High update rates suitable for UAVs and advanced air mobility (AAM) platforms

A simplified Cramér–Rao lower bound (CRLB) illustrates how antenna geometry and signal power influence the accuracy of AoA estimation:

3.3 Infrastructure Density and Geometric Strength

From a PNT perspective, measurement geometry can be as important as measurement precision. Dense deployments of base stations, small cells, and access points give 5G, 6G, and Wi-Fi networks inherently strong geometric diversity, especially in environments where GNSS geometry collapses.

In indoor settings or street canyons, a receiver may have ten or more RF sources within a few hundred meters. This density improves dilution of precision (DOP), increases redundancy, and enables fallback positioning even when GNSS availability drops to zero. Within the multi-layer architecture described in Figure 1, terrestrial networks therefore provide crucial observability in GNSS-restricted environments.

3.4 High Signal Power and Robust Tracking

Terrestrial and LEO communication signals enjoy a link-budget advantage of roughly 50–100 dB over GNSS. This additional power yields several practical benefits:

• Better performance with small or non-ideal antennas
• Increased resilience to interference and jamming
• Faster acquisition and re-acquisition after outages
• More reliable tracking under fast dynamics or partial obstruction

In many scenarios, 5G, Wi-Fi, and LEO signals remain trackable long after GNSS signals fall below usable thresholds, providing essential continuity for navigation filters and multi-sensor fusion engines.

3.5 Timing and Synchronization in Communication Networks

Modern wireless networks rely on tight synchronization for scheduling, beamforming, and coordinated MIMO. They obtain timing from GNSS, fiber distribution, and packet-based protocols such as IEEE 1588 Precision Time Protocol (PTP) [9]. As these timing infrastructures mature, communication networks increasingly become timing providers rather than solely timing consumers.

Although terrestrial networks do not yet match the long-term stability of GNSS-disciplined oscillators, they provide valuable short-term holdover and regional timing continuity. These capabilities play an important role in multi-layer PNT systems, particularly during GNSS outages.

4. Engineering Challenges and Limitations

Although communications-based PNT provides powerful complementary capabilities, significant engineering challenges remain. These challenges do not diminish the value of multi-layer PNT; rather, they highlight the technical rigor required to deploy these systems reliably on a scale.

4.1 Multipath and Non-Line-of-Sight Propagation

For terrestrial PNT, multipath and non-LOS propagation remain the dominant contributors to ranging and angle errors. Buildings, vehicles, reflective indoor structures, and metallic industrial environments introduce secondary paths that bias ToA, RTT, AoA, and Doppler measurements. A simplified model of multipath-induced ToA bias is:

Massive MIMO beamforming, high-resolution channel estimation, and machine-learning LOS classifiers can mitigate these errors, but performance is highly environment-dependent and cannot be guaranteed in all cases. Figure 3, introduced earlier, highlights how diversity in measurement types helps reduce susceptibility to any single error mechanism.

4.2 Synchronization Constraints and Timing Drift

Communication networks require precise time alignment for scheduling, beamforming, and coordinated MIMO. However, network clocks do not yet match the long-term stability of GNSS-disciplined oscillators. Backhaul delay variability, oscillator drift, and partial GNSS visibility at base stations introduce timing uncertainty that must be explicitly modeled in a PNT fusion engine.

Figure 4 illustrates timing error growth during a GNSS outage, comparing:

• GNSS-only timing, which diverges quickly without satellite visibility
• Network timing holdover, which slows but does not halt drift
• Multi-layer timing fusion, which maintains the lowest error accumulation

These behaviors demonstrate why communication-based timing is best used as a complementary layer rather than a standalone reference.

Figure 4. Timing error comparison during a GNSS timing outage.

4.3 Waveform and Structural Limitations

Modern communication waveforms such as OFDM were optimized for throughput and spectral efficiency, not navigation. Several characteristics constrain raw positioning performance:

• Finite pilot density limits effective ranging bandwidth
• High peak-to-average power ratio (PAPR) stresses nonlinear receivers
• Cyclic prefix duration restricts ToA resolution
• TDD reciprocity assumptions introduce calibration-dependent biases

4.4 Coverage Variability and Regulatory Constraints

Terrestrial network density varies sharply by geography. Urban cores, industrial sites, and indoor campuses enjoy strong 5G/6G and Wi-Fi coverage, whereas rural, maritime, and mountainous regions may see limited improvements without LEO-PNT augmentation. Spectrum policy, privacy rules, and operator-controlled access to timing and positioning features further constrain how widely these capabilities can be exposed. Figure 5 summarizes the relative contribution of each PNT layer—GNSS, LEO-PNT, terrestrial networks, and onboard sensors—across open-sky, urban, and indoor environments.

Figure 5. Relative contribution of PNT layers across operational environments.

4.5 Security and integrity

As communication signals begin supporting navigation functions, they must meet higher standards for robustness, integrity, and security. PNT observables are vulnerable to spoofing, replay, meaconing and cyber-attacks on timing sources [10]. GNSS experience demonstrates the value of:

• Cross-layer consistency checks
• Cryptographic authentication
• Fault detection and exclusion (FDE)
• Monitoring for anomalies in Doppler, timing, or angle domains
• Redundancy across multiple constellations and layers.

These functions are visualized in Figure 6, which illustrates how a multi-layer PNT system performs integrity monitoring across heterogeneous measurements.

5. A Multi-Layer Architecture for Future PNT

The earlier sections described why GNSS alone cannot meet emerging PNT requirements and how communications and LEO signals provide new sources of observability. Building on those foundations, Figure 1 introduces a multi-layer architecture in which GNSS, LEO-PNT, terrestrial networks, and onboard sensors cooperate to deliver resilient positioning and timing. This section outlines the role of each layer and how they integrate into a unified system.

5.1 GNSS as the Foundational Global Layer

GNSS will continue to provide the global reference frame, absolute positioning, and precise timing that anchor the entire architecture. Its worldwide availability, mature error modeling, and extensive user base make it the natural reference for other layers to align with whenever GNSS is available and reliable. In this sense, GNSS remains the “truth model” for time and coordinates, even as additional layers enhance resilience.

5.2 LEO-PNT as the High-Power, High-Dynamics Space Layer

LEO satellites provide diversity in orbit, signal power, geometry, and dynamics. Their lower altitude results in significantly stronger signals and rapid Doppler variations that improve motion observability. These characteristics reinforce GNSS performance in interference, urban canyon, and high-dynamics environments. As shown in Figure 3, LEO adds Doppler-based range-rate observables that are particularly valuable for maintaining continuity when GNSS quality fluctuates.

5.3 Terrestrial Networks as the Urban and Indoor Layer

5G, Wi-Fi 7, and future 6G networks form the densest PNT-capable infrastructure ever deployed. Their wideband signals, massive MIMO arrays, and strong received power position them as the dominant layer for indoor and urban navigation. Where GNSS geometry collapses, terrestrial networks provide ToA, AoA, AoD, RTT, and coverage exactly where users most often need it. Figure 5 highlights how their contribution becomes primary indoors and highly complementary in urban canyons.

5.4 Onboard Sensors and Local References

IMUs, odometry, barometers, cameras, radar, and lidar provide short-term stability and immediate awareness of the immediate environment, independent of external RF conditions. These sensors bridge outages and reduce reliance on any single external signal source. Their role within architecture mirrors their role in autonomy today: providing the continuity needed when GNSS, LEO, or terrestrial signals fluctuate. Together with RF observables, they form a robust solution space consistent with the measurement diversity shown in Figure 3.

5.5 Fusion, Standards, and System Engineering

Realizing a multi-layer PNT system is fundamentally a system-engineering effort. Success depends on:

• Common timing and reference frameworks across GNSS, LEO, and terrestrial layers
• Standardized quality indicators and integrity metrics
• Interfaces that expose PNT-relevant observables from communication networks while respecting privacy and operational constraints
• Cross-layer consistency checks that ensure no single measurement dominates unchecked

Standards bodies, including 3GPP, IEEE and aviation authorities, are beginning to address these needs, but operationalizing multi-layer PNT at scale will require continued collaboration across industries. Figure 6 illustrates how integrity information from each layer contributes to fault detection, cross-checking, and integrity-bound estimation within the fusion engine.

6. Conclusion

The era of single-layer PNT is coming to an end. As reliance on precise positioning and timing accelerates across aviation, ground autonomy, critical infrastructure, and networked systems, GNSS alone cannot shoulder the growing resilience burden. Fortunately, a rich set of complementary technologies already surrounds us. Dense terrestrial networks, emerging LEO constellations, and increasingly capable onboard sensors provide observables that naturally augment GNSS and extend PNT into environments where satellite signals struggle.

The opportunity now is to treat communications and PNT not as separate domains but as elements of a unified system. A multi-layer architecture — such as the one outlined in this article — offers stronger availability, improved measurement diversity, and inherent resilience against interference, outages, and environmental constraints. The key challenge ahead lies not in inventing new signals, but in system engineering: establishing shared timing frameworks, standardizing measurement interfaces, ensuring integrity across heterogeneous sources, and building trust in signals not originally designed for navigation.

Most of the technical ingredients are already in place. The next decade will determine how effectively industry, government, research institutions, and standards bodies can integrate them into certifiable, interoperable, and widely deployable solutions. If successful, multi-layer PNT will become a foundational capability — providing trustworthy positioning and timing wherever future autonomous systems, vehicles, and critical infrastructure require it.