C-V2X: Cellular Vehicle-to-Everything Tech

• C-V2X is standardized wireless technology under 3GPP that uses both direct sidelink (PC5) and network-based (Uu) interfaces to connect vehicles, infrastructure, and pedestrians.
• It employs advanced resource allocation methods, including semi-persistent scheduling and dynamic congestion control, to support high mobility and dense environments.
• Its evolution from LTE-V2X to 5G/NR-V2X enhances URLLC, multi-connectivity, security, and edge computing integration for autonomous driving and intelligent transportation.

Cellular Vehicle-to-Everything (C-V2X) is a standardized wireless communication technology developed under 3GPP Releases 14 and beyond to enable direct and network-assisted connectivity among vehicles, infrastructure, pedestrians, and networks using cellular radio interfaces. Architected to support high mobility, massive density, and stringent reliability/latency requirements, C-V2X underpins road safety, autonomous operation, and advanced transportation objectives in 5G and B5G intelligent transportation systems.

1. System Architecture and Connectivity Paradigms

C-V2X employs two orthogonal air interfaces:

A. PC5 Sidelink (Direct/Autonomous Mode):

• Supports direct V2V, V2I, and V2P over the PC5 interface, operating in both in-coverage and out-of-coverage scenarios.
• 3GPP Mode 4 (in LTE, Release 14): Decentralized, distributed medium access using Sensing-Based Semi-Persistent Scheduling (SB-SPS); UEs sense a 1000 ms history window, build candidate resource sets, and stochastically reserve periodic transmission opportunities.
• 3GPP NR-V2X Mode 2: Extends autonomy with enhanced resource pool configuration, supporting both periodic and aperiodic traffic, long- and short-term sensing.

B. Uu Cellular Link (Network-Based Mode):

• Enables V2N via uplink/downlink through eNB/gNB to the cellular core.
• 3GPP Mode 3 (in LTE, Release 14): Network-controlled, semi-persistent or dynamic scheduling of sidelink resources; eNB/gNB allocates resources and controls priority.
• NR-V2X Mode 1: gNB-based dynamic/configured grant for centralized, low-latency resource management.

The resulting architecture supports harmonized direct/indirect traffic flows, with flexibility for broadcast, multicast, groupcast, and unicast operation, and accommodates advanced use cases such as platooning and collective perception (Tahi, 2024, Shah et al., 2023, Chen et al., 2020).

2. Multi-Connectivity in Downlink C-V2X: Analytical Models and Coverage

In the context of 5G/B5G, multi-connectivity—where each vehicle simultaneously receives from the m nearest downlink base stations (DBSs)—enables significant improvement in coverage probability. The canonical analytical framework (Jiao et al., 2024) is as follows:

• Spatial Model: Vehicles on a 1D line (road), DBSs as a 1D homogeneous Poisson point process (PPP) of density λ_d (nodes/km). Vehicles form an independent 1D PPP of intensity λ_v.
• Connectivity: Each vehicle connects with the m nearest DBSs, which jointly transmit to it (signal field network, SFN combining); remaining DBSs are treated as interferers.
• Channel Model: Path loss ℓ(r) = r^{-α_d} (α_d > 2), Rayleigh fading, and log-normal shadowing χ_d; random-displacement is applied to model shadowing-induced distance stretch.
• SINR at the origin:

$$\mathrm{SINR}_D = \frac{\sum_{i=1}^m P_d\,g_{d,i}\,x_i^{−α_d}}{\sum_{x\in\Phi_D^t\setminusϕ_c}P_d\,g_{d}\,|x|^{−α_d} + σ_d^2}$$

• Coverage Probability:

$P_{cov}(t) = \Pr(\mathrm{SINR}_D > t )$

is evaluated by integrating over the joint PDF of DBS distances and the Laplace transform of interference.

Key findings include:

• Increases in m monotonically boost coverage, but with diminishing returns.
• A higher path loss exponent α_d strengthens coverage by suppressing interference more rapidly with distance.
• Optimal λ_d depends on the interplay of cooperation and interference; over- densification can cause saturation or collapse due to interference (Jiao et al., 2024).

Monte Carlo analysis confirms the accuracy of these stochastic geometry-based predictions and supports dimensioning guidelines for multi-connectivity deployment.

3. Medium Access, Resource Allocation, and Congestion Control

C-V2X’s resource allocation algorithms are required to meet highly dynamic topology, diverse quality of service (QoS), and spectrum-scarcity constraints (Tahi, 2024, Shah et al., 2023, Bahonar et al., 2021, Toghi et al., 2018).

• Semi-Persistent Scheduling (SPS): Used in both centralized (Mode 3/1) and distributed (Mode 4/2) regimes.
  • Distributed (SB-SPS): Each UE senses channel history, filters and ranks resources (lowest RSRP/A-RSSI), randomly selects from the best 20%, and reserves them for multiple consecutive transmissions.
  • Centralized: eNB/gNB computes the global optimal assignment, typically via mixed-integer programming or hierarchical algorithms.
  • Optimizations: Enhanced Mode 4 can include explicit sharing of reselection counters and staggered reservation mapping functions to reduce collision-induced outages and minimize age of information (AoI) (Chu et al., 2023).
• Congestion Control: SAE J2945/1-style distributed congestion control (DCC) governs transmit inter-packet time (rate control) and transmit power (range control) based on channel busy percentage (CBP). Empirically, rate control is critical in preventing network collapse under saturation; power control alone is less effective in high density. Adaptive rate/power adjustments are tuned to maintain channel occupancy within prescribed bounds and react to network load (Toghi et al., 2019, Toghi et al., 2019, Saifuddin et al., 2020).

In high density, performance degrades due to persistent collisions and half-duplex constraints, mandating unified parameter tuning (e.g., RSRP thresholds, resource pool size, reselection probabilities) and, where available, centralized scheduling (Toghi et al., 2018, Shah et al., 2023).

4. Latency, Reliability, and Advanced Performance Metrics

Ultra-reliable low-latency communication (URLLC) for C-V2X safety applications imposes strict requirements:

• End-to-end (E2E) latency: Sub-10 ms (safety-critical) to 100 ms (infotainment), with round-trip latencies further lowered via Multi-access Edge Computing (MEC).
• Reliability: Five-9s (99.999%) packet delivery ratio at close/mid ranges; empirical PRR >90% at 100–300 m under well-provisioned settings (Gill, 2022, Wang et al., 2019).
• Metrics such as AoI and system-level AoI quantify the freshness of status update flows, with recent enhancements improving AoI through explicit resource reservation mapping and control information sharing (Chu et al., 2023).
• Channel model selection (e.g., 3GPP Release 15 vs. WINNER II) and system provisioning (bandwidth, density, MCS) are critical; only benign environments with sufficient bandwidth (≥8–10 MHz) meet URLLC targets at scale (Wang et al., 2019, Wang et al., 2019).

5. Evolution from LTE-V2X to 5G/NR-V2X

The evolution from LTE-V2X (R14/15) to NR-V2X (5G, R16+) introduces (Chen et al., 2020, Du et al., 2021, Shah et al., 2023):

• Flexible numerology (slot and mini-slot), supporting sub-ms transmission and pre-emption.
• Advanced MAC: Network-assisted and enhanced distributed scheduling, cross-mode harmonization, grant-free and dynamic grant access, HARQ feedback for group/unicast.
• Sidelink spectrum up to 40 MHz (R16), network slicing, and edge computing integration.
• “Enhanced V2X” use cases (eV2X): Platooning, remote/collective driving, extended sensors, with URLLC grade reliability and very high throughput demands.
• Coexistence with DSRC/WAVE and cross-technology spectrum sharing requires adaptive resource management and coordinated interference sensing.

6. Security, Privacy, and Physical Layer Security Techniques

C-V2X combines classic cellular security mechanisms with V2X-specific enhancements (Sharma et al., 2019, Marojevic, 2018, Wang et al., 2020):

• LTE-V2X: EPS-AKA, group keys for PC5, limited end-to-end encryption/integrity on sidelink. No mandatory security in Mode 4; application-layer signatures (ECDSA, IEEE 1609.2) and symmetric group keys (TESLA-style) are commonly proposed.
• 5G/NR-V2X: SBA core, localized (edge) authentication, pseudonym management, per-slice keys, and the proposed Security Reflex Function for rapid, on-demand authentication/slicing at the edge.
• PLS: Physical layer security enhancements using artificial noise and secure beamforming, leveraging stochastic geometry models for secrecy probability and effective secrecy throughput; optimal trade-offs are obtained by power-splitting between data and AN, with gains scaling in antenna array size but saturating beyond moderate N (Wang et al., 2020).
• Resilience to RF attacks (jamming, replay, Sybil) is supported by protocol-level and PHY-layer anomaly detectors, along with continuous credential rotation (Marojevic, 2018, Sharma et al., 2019).

7. Practical Deployment, Field Trials, and Environmental Impact

Empirical analyses confirm extended range, lower latency, and higher PRR for C-V2X compared to DSRC in various field trials and system-level simulations (Chen et al., 2020, Du et al., 2021, Wang et al., 2019, Wang et al., 2019). Key findings:

• Market penetration above 10–30% is necessary for ecosystem-level safety and efficiency benefits; gains plateau (or for naive schedulers degrade) at excessive penetration rates without scheduled resource allocation.
• MEC is critical for reducing urban V2P latencies by ≈75% (Gill, 2022).
• C-V2X-enabled applications (CACC, GLOSA, eco-routing) yield up to 31% fuel and CO2 savings, with up to 20% less energy use under high penetration (Du et al., 2021).
• National-scale deployment testing (e.g., China) demonstrates stable, high-rate throughput, >99% reliability, and validates interoperability in large OEM and supplier consortia (Chen et al., 2020).

Persistent open problems include cross-technology harmonization, ultra-dense security and handover, human-in-the-loop studies, and scalable joint communication-computation optimization.

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References

(Jiao et al., 2024): Coverage Analysis of Downlink Transmission in Multi-Connectivity Cellular V2X Networks (Tahi, 2024): Resource Allocation in C-V2X: A review (Shah et al., 2023): Scalable Cellular V2X Solutions: Large-Scale Deployment Challenges of Connected Vehicle Safety Networks (Bahonar et al., 2021): Low-Complexity Resource Allocation for Dense Cellular Vehicle-to-Everything (C-V2X) Communications (Chia-Hung et al., 2021): A C-V2X Platform Using Transportation Data and Spectrum-Aware Sidelink Access (Gill, 2022): Latency Analysis of Vehicle-to-Pedestrian C-V2X Communications at Urban Street Intersections (Chu et al., 2023): Enhanced C-V2X Mode 4 to Optimize Age of Information and Reliability for IoV (Toghi et al., 2019): Analysis of Distributed Congestion Control in Cellular Vehicle-to-everything Networks (Toghi et al., 2019): Spatio-temporal Dynamics of Cellular V2X Communication in Dense Vehicular Networks (Toghi et al., 2018): Multiple Access in Cellular V2X: Performance Analysis in Highly Congested Vehicular Networks (Wang et al., 2019): Performances of C-V2X Communication on Highway under Varying Channel Propagation Models (Wang et al., 2019): System-Level Simulator of LTE Sidelink C-V2X Communication for 5G (Saifuddin et al., 2020): Performance Analysis of Cellular-V2X with Adaptive and Selective Power Control (Wang et al., 2020): Physical Layer Security Enhancement Using Artificial Noise in Cellular Vehicle-to-Everything (C-V2X) Networks (Du et al., 2021): Environmental and Safety Impacts of Vehicle-to-Everything Enabled Applications: A Review of State-of-the-Art Studies (Chen et al., 2020): A Vision of C-V2X: Technologies, Field Testing and Challenges with Chinese Development (Sharma et al., 2019): Security of 5G-V2X: Technologies, Standardization and Research Directions (Marojevic, 2018): C-V2X Security Requirements and Procedures: Survey and Research Directions