Tony and Arne [1] investigated schemes for supporting multirate transmissions in DS-CDMA systems, addressing challenges related to managing multiple information streams at varying speeds. Their work proposed modulation and coding-based solutions that enhanced spectral efficiency while ensuring reliable communication. These foundational contributions remain critical to understanding the coexistence and performance of multirate services in wireless networks.

Zhang et al. [2] further extended this domain by proposing a MAC layer design leveraging multi-user detection techniques. Their approach reduced multiple access interference and optimized resource management, achieving improved throughput and interference tolerance. These findings underscored the importance of tightly integrating MAC and PHY layers to enhance the efficiency of decentralized networks.

Chamola et al. [3] conducted a comprehensive analysis of vulnerabilities in unmanned aerial vehicle (UAV) networks, focusing on cyber and physical threats. They proposed advanced detection algorithms and adaptive defense strategies tailored for critical applications such as disaster relief and military operations. This study emphasized the necessity of secure and flexible protocols to address unique challenges in UAV networks.

Verdú [6] introduced groundbreaking concepts in multiuser detection, providing a theoretical framework for designing cross-layer protocols that optimize system performance under varying conditions. By integrating PHY and MAC layer interactions, these cross-layer mechanisms enable robust performance improvements in dynamic environments.

Table 1 provides a comparative summary of key studies, highlighting their strengths, limitations and relevance to the current work.

Building on these foundational works, the present study explores the integration of prediction-based rate adaptation and advanced receiver techniques within VANETs. Unlike prior research, this work emphasizes dynamic environments characterized by high vehicular mobility and varying channel conditions. By leveraging insights from multiuser detection [6] and adaptive management strategies [2] , the proposed framework addresses both performance optimization and computational complexity.

This section describes the proposed cross-layer mechanism, wherein transmission parameters are adjusted through collaboration between the physical layer and the medium access control (MAC) layer based on channel prediction. Figure 1 illustrates the conceptual framework.

Parameter updates are managed by the MAC layer and depend on the employed transmission technique:

The transmitter employs a variable spreading factor, resulting in variable packet lengths. Examples include:

The computational complexity of the proposed cross-layer mechanism is primarily driven by the prediction algorithm and parameter update frequency. Techniques like LMS prediction, as described in [1] , offer a balance between computational efficiency and accuracy. Scalability is ensured by modular design, allowing integration across diverse vehicular ad-hoc networks (VANET) platforms [6] .

Real-world implementation may face challenges such as hardware limitations and channel variability. SDR platforms provide a flexible testing environment, but constraints like processing power and memory can impact performance [3] . Optimization techniques are necessary to ensure real-time operation.

Cross-layer coordination ensures seamless interaction between the physical and MAC layers. This approach improves system performance by dynamically adjusting transmission parameters based on real-time network conditions.

Figure 2 illustrates the framework for cross-layer coordination in dynamic VANET environments. The process consists of the following key steps:

This iterative process enables the system to adapt effectively to varying network conditions, ensuring optimal performance in terms of throughput, latency and reliability.

Compared to traditional layered approaches, cross-layer coordination mitigates inefficiencies by enabling informed decisions on transmission parameters [2] . For example:

In dynamic VANET environments, the framework adapts to conditions such as:

This adaptability enhances QoS by optimizing throughput and reducing latency [3] [6] .

In the proposed cross-layer framework, seamless and efficient communication between the physical layer and the medium access control (MAC) layer is critical. This section outlines the mechanisms of information exchange, potential challenges and the role of prediction accuracy in enhancing system performance.

Information exchanged between the layers includes:

- Pilot bits for channel prediction.

- Required packet loss rates.

- Desired transmission rates.

- Predicted physical channel gains.

- Corresponding transmission power levels.

- Available packet loss rates.

- Offered transmission rates.

This bidirectional communication ensures that both the physical and MAC layers work cohesively to optimize transmission parameters based on real-time channel conditions.

Several challenges must be addressed to achieve effective inter-layer communication:

1) Latency: Delays in information exchange can lead to outdated decisions, reducing the effectiveness of adaptive mechanisms.

2) Synchronization: Ensuring synchronization between layers is critical to avoid inconsistencies in parameter adjustments.

3) Scalability: The framework must handle increasing numbers of users and devices without significant performance degradation.

4) Hardware Limitations: Processing and memory constraints can limit the ability to compute and exchange data efficiently.

Prediction accuracy directly impacts system performance. Accurate channel predictions allow for:

Table 2 summarizes the key information exchanged between layers.

The primary objective is to maximize spectral efficiency by dynamically adapting transmission rates to varying channel conditions. This is achieved through:

Key benefits include:

To evaluate the system performance, we analyze a CDMA platform incorporating the four receiver filters described in Section 2.1. The platform uses a variable spreading factor transmitter, which reflects realistic scenarios in VANET environments where adaptive communication techniques are required to handle dynamic traffic and channel conditions.

The parameters used in our simulations are summarized in Table 3 . These parameters were carefully chosen to reflect real-world VANET environments, considering factors such as signal bandwidth, power constraints and the diversity of communication conditions encountered in vehicular networks. For example, the use of multiple code lengths and variable constellations mirrors the flexibility required for handling diverse data rates and quality of service (QoS) requirements in VANETs.

The inclusion of parameters such as pilot bits and prediction filters reflects practical requirements for ensuring reliable channel estimation and adaptive rate

control, both of which are critical in dynamic vehicular scenarios. These parameters ensure the simulation’s results are transferable to real-world deployments.

Table 4 outlines the simulated channel conditions and key parameters. These simulations explore different scenarios to evaluate system performance under varying bandwidth and multipath environments, which are common in urban and highway VANET settings.

Narrowband CDMA channels are utilized for their simplicity and efficiency in demodulation due to their flat frequency response, making them a suitable choice for modeling basic communication scenarios. However, the limited data rates offered by such channels necessitate the exploration of broadband multipath channels.

Broadband multipath channels, characterized by frequency-selective fading, provide a more realistic reflection of urban VANET environments, where signals arrive through multiple paths due to reflections and scatterings. These conditions simulate the challenges faced in real-world vehicular communication systems, such as interference and packet loss.

By incorporating both channel types, the simulations offer a comprehensive evaluation of system performance across varying conditions, enabling a robust analysis of the proposed framework’s adaptability and efficiency.

In the broadband context, the Rayleigh multipath channel is modeled as a collection of signals with random delays, where the path amplitudes follow a Rayleigh distribution. The channel model is expressed as:

$h_k\left(t\right)=\underset{l=1}{\overset{L_p}{{\displaystyle \sum }}}{\alpha }_{k,l}\delta \left(t-{\tau }_{k,l}\right)$(1)

where:

The RAKE receiver is employed to process multipath signals by combining the energy from multiple paths, thereby improving signal power. The number of detectable paths $L$ can be calculated as:

$L_{\text{max}}\le \frac{{\tau }_{\text{max}}}{T_c}+1$(2)

where ${\tau }_{\text{max}}$ is the delay spread and $T_c$ is the chip duration.

Two combining techniques are utilized:

While the Rayleigh multipath channel model effectively captures the statistical properties of wireless fading environments, its predictions have been validated against experimental data in controlled scenarios. Studies show that the model accurately predicts signal attenuation and interference in urban environments with dense multipath components. However, in rural or sparse multipath environments, the model’s assumptions about uniform delay distribution may diverge from real-world observations, leading to overestimated signal variations.

The Rayleigh model assumes a large number of independently fading paths, which is not always representative of real-world environments:

These limitations can affect the interpretation of simulation results:

Despite these limitations, the Rayleigh multipath channel model remains a widely used tool for analyzing and optimizing wireless communication systems, particularly when combined with appropriate adjustments to account for real-world deviations.

Evaluating the performance of dual-layer transmissions requires robust metrics to quantify system efficiency and reliability under varying network conditions. This subsection examines key metrics, including signal-to-interference-plus-noise ratio (SINR), bit error rate (BER) and packet reception rate, to analyze the effectiveness of the proposed transmission techniques across different receiver configurations and modulation schemes.

For variable spreading factor transmissions, the Signal-to-Interference-plus-Noise Ratio (SNR) for user $k$ is expressed as:

${\text{SINR}}_{k,\text{MF}}=\frac{P_k}{N_0+{{\displaystyle \sum }}_{j\ne k}\frac{P_j}{S{F}_j}}$(3)

where:

For multiple code transmissions, the Bit Error Rate (BER) is determined using the SNR expressions tailored for each detector type:

${\text{SINR}}_{k,\text{MF}}=\frac{P_k}{N_0+{{\displaystyle \sum }}_j{P}_j}$(4)

${\text{SINR}}_{k,\text{SIC}}=\text{Recursive Function of Interference Order}$(5)

For modulation schemes with variable constellation sizes (e.g., M-QAM), the symbol error probability $P_s$ is expressed as:

$P_s=\frac{2}{{\text{log}}_2\left(M\right)}\left(1-\frac{1}{\sqrt{M}}\right)Q\left(\sqrt{\frac{3}{M-1}\text{SINR}}\right)$(6)

The binary error probability is derived from the symbol error probability using the relation:

$P_b=\frac{P_s}{{\text{log}}_2\left(M\right)}$(7)

Efficient rate adaptation is crucial for maintaining optimal performance in VANETs, where dynamic mobility and varying channel conditions significantly impact the access layer’s ability to deliver reliable communication and maximize throughput.

In a 25 MHz multipath fixed-to-mobile channel, performance varies significantly depending on the transmission technique:

The decorrelator receiver demonstrates robust performance across all three transmission techniques, achieving a packet reception rate of 86% - 95% with both SIC (Successive Interference Cancellation) and decorrelator receivers.

Notably, the decorrelator can operate effectively regardless of the transmission technique, offering high reliability in all cases. The diversity techniques used significantly improve both reception quality and the aggregate useful data rate at the receiver node.

The performance of different receiver configurations, including MMSE, decorrelator,

SIC and matched filter, is analyzed across various transmission techniques to evaluate their effectiveness in handling interference, improving packet reception rates and maximizing throughput under diverse channel conditions.

1) Multi-Factor Spreading Transmission

In multi-factor spreading transmission, results depend on channel conditions:

- MMSE (Minimum Mean Square Error) and decorrelator receivers achieve nearly identical packet reception rates and average useful bit rates.

- The MMSE detector slightly outperforms the decorrelator.

- These two detectors double the performance of the SIC detector.

- The decorrelator and SIC receivers perform relatively well.

- Overall, multi-rate transmissions decoded by multi-user detection provide better performance than those using matched filter receivers.

2) Multi-Code Transmission

In multi-code transmission, performance improves slightly compared to multi-factor spreading:

- The MMSE and decorrelator receivers achieve nearly identical results, offering the best performance.

- The matched filter receiver performs the worst.

- The SIC detector ranks between these two levels.

- The decorrelator detector delivers the highest packet reception rates and useful bit rates, significantly outperforming the other detectors.

- The SIC detector achieves moderate performance, as multiple access interference increases with the number of codes.

- The matched filter receiver exhibits low performance due to limited interference rejection capability under multi-code conditions, which is less effective than under multi-factor spreading.

3) Variable Constellation Size Transmission

For variable constellation size transmission, results are generally weaker compared to the other two transmission techniques:

(a) MMSE detector (effective in the first channel).

(b) Decorrelator receiver (effective across all channels).