Security and Efficiency Trade-Offs in Mixed Cooperative Relay

Systems with Eavesdropper Interference

Deepak Upadhyay

, Mridul Gupta

and Nookala Venu

Dept. of Computer Science and Engineering, Graphic Era Hill University, Dehradun, India

Dept. of Electronics and Communication Engineering, Graphic Era deemed to be University, Dehradun, India

Centre for Internet of Things, Madhav Institute of Technology & Science, Deemed University, Gwalior, India

Keywords: Eavesdropper Interference, Wireless, DSP.

Abstract: These simulations give a good overall impression of how various aspects affect relay network performance.

Highlights: Trade-off between secrecy capacity and signal-to-noise ratio effective; decode-and-forward

strategies usually better than amplify-and-forward Addition of more eavesdroppers increases the secrecy

outage probability, making it necessary to find an optimal positioning for relay node. Initial energy efficiency

versus relay density analysis reveals inherent trade-offs, and latency results suggest that more secure protocols

could raise latencies under active eavesdropper interference. The heatmap on channel fading effects has

different impacts regarding secrecy capacity and efficiency, while the investigation of multiple-hop

interference demonstrates the success of beamforming or advanced management techniques. Such results help

in designing safe and high-speed relay networks.

1 INTRODUCTION

Wireless communication systems are an interesting

study topic largely due to their vast applications

covering but not limited to cell networks and the

Internet of Things (IoT). In this respect (Nguyen, et

al. 2023), the use of cooperative relay systems has

appeared to be an important way to increase

communication performance. Relays assist in data

transfer from the source to destination which helps to

avoid path losses and enhance total system

performance known as cooperative relaying strategy.

Achieving these paradigms, however, is not free of

challenges. Security is also a major concern due to the

Eavesdrop (Upadhyay, Upadhyay, et al. 2024)

interference and one of the trade-off problems

between security and efficiency (Chu, Qiu, et al.

2021).

Relay enhanced systems are designed for

cooperative relaying where security is one of the

foremost concerns nowadays because information

can be stolen in a way that it remains unauthorized

(Upadhyay, et al. 2024). Eavesdroppers who sniffer

communication channels can significantly reduce the

confidentiality of data that is in transit. Given the

security threat presented by these malicious entities,

robust mechanisms are needed that can prevent any

unauthorized access to confidential information.

Further complexity in the security challenge derives

from mixed cooperative relay (Xu, Song, et al. 2020)

systems, where both decode-and-forward (DF) and

amplify-and-forward (AF), i.e., different types of

relaying strategies are used within one network.

Every relay strategy has its own advantages and

disadvantages, especially in terms of the security-

efficiency trade-off they strike (Li , et al. 2020)

In the decode-and-forward strategy, relays first

decode received signal and then forward to

destination. The inherent error correction capabilities

at the relay improve signal quality but come with

added computational complexity and delay. Contrary

to this, the amplify-and-forward technique permits

relays only to amplify signal which delivers towards

it. While this method is simplified and speeds up the

process (Cai, Ma, et al. 2023), it also enhances any

noise or interference existing in the original signal

which can reduce security of transmission. Mixed

cooperative relay systems considered have the

advantages of both strategies and can achieve an

improved performance in these conditions by

selecting relaying method over channel fading state

and security demand (Nguyen, She, 2023).

Upadhyay, D., Gupta, M. and Venu, N.

Security and Efﬁciency Trade-Offs in Mixed Cooperative Relay Systems with Eavesdropper Interference.

DOI: 10.5220/0013635400004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 3, pages 651-658

ISBN: 978-989-758-763-4

651

Finally, the consideration of eavesdroppers

imposes an outer bound for the security efficiency

trade-offs. Privacy is essential for cooperative relay

systems when eavesdroppers are attempting to

intercept communication; thus, we need a secure

scheme while maintaining high efficiency properties

Physical layer security (Upadhyay, Tiwari, et al.

2022) methods are an option to overcome this

challenge. These techniques exploit the characteristic

nature of wireless channels like fading and noise to

enhance security. These strategies allow design and

resource allocation which provides maximum secrecy

capacity of the system in terms of transmission power

and relay selection— the rate at which secure

information can be transmitted with a guaranteed

reliability without being detected by an eavesdropper

(Upadhyay, Tiwari, et al. 2022)

This work establishes that by integrating

stochastic geometry and game-theoretic mode in

recent research could provide a better understanding

of the performance capabilities of cooperative relay

systems compromised to eavesdropper interception.

To investigate these metrics, stochastic geometry has

proven to be a powerful tool for modeling and

analysis of the corresponding spatial distribution of

nodes in wireless networks. Such metrics are

important to assess how well a mixed cooperation-

based relay is able to perform and direct its security-

optimized operation (Upadhyay, Gupta, et al. 2023).

However, game-theoretic models provide the

framework to formulate the strategic interaction

between optimal strategies of resource allocation and

relay selection from one side legitimate user while

that other eavesdropper (Vimal, et al. 2021).

Additionally, the use of machine learning in

cooperative relay systems has shown new doors

towards security and efficiency. These machine

learning algorithms learn the best relay schemes for

maximal secrecy capacity and minimal interference

against eavesdropping over time by dynamically

adjusting to different network conditions (Vimal,

Nigam, et al. 2018). Such as using reinforcement

learning to enable the system to conduct relay

selection and power allocation action at each

available slot, an efficient way would be proposed for

eavesdropping behavior via a series of N attacks that

can individually occur within low security standard

but together are challenging.

In mixed cooperative relay systems, the trade-off

between security and efficiency is interlaced with

architectural-related aspects of the network and

environmental factors in which it operates. For

example, a dense urban environment with many

obstructions results in complex multipath scenarios

[11] that impact the propagation characteristics of the

signals. In such environments, the deployment of

relay nodes must be scheduled carefully to guarantee

an equal as possible distribution in terms of providing

coverage and security. Again, in terms of ever-

changing networks topology with fast changing time

variables (VANET), the system must respond quickly

as well regarding adjusting relay strategies for

achieving efficiency and simultaneity.

The security efficiency trade-off is not only

influenced by technological and architectural

requirements but also a combination of

regulations/policymaking ideologies. Wireless

communication systems are on the rise and

consequently, governments and regulatory bodies

have created strict measures to keep user data

confidential while maintaining secure

communications. This would mean that compliance

with these regulations could impose stringent security

requirements on network operators and reduce the

system efficiency [4]. In this sense, combining

security and efficiency goes together with a good

understanding of what technology is doing in terms

of providing solutions while also maintaining

compliance to regulations.

In this context, mixed cooperative relay systems

exhibit simple structure due to involving both DF and

CRTC in the same system which leads to further

challenges in pursuing an optimal security-efficiency

tradeoff. Improved safety measures would result in a

network that is tougher and could hold up important

utilities such as crisis services, business

communications and army comms. Meanwhile,

efficiency improvements are also essential to enable

these systems to support the constantly increasing

demand for high-speed data transmission in an

energy-efficient way [5].

Summarizing, the interaction between security

and efficiency in mixed cooperative relay systems

with eavesdropped nodes is a complicated issue that

needs to be considered all together. We have

demonstrated how systems can be designed to create

a robust defense against eavesdropper Interference

leveraging advanced physical layer security

techniques along with machine learning algorithms

and strategic relay deployment, while also

maintaining very high efficiency [2]. Emerging

solutions are expected in due course when research

evolves further to offer practical mechanisms that can

help address such security concerns on modern

wireless communication networks -this is key for

leading the way towards a new age of secure- and

energy-efficient communication systems.

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2 METHODOLOGY

The methodology outlined below provides a

structured approach to implementing simulations for

evaluating security and efficiency in relay networks.

The chosen algorithms and steps are detailed to

ensure a comprehensive understanding of the

simulation process.

2.1 Algorithm Selection

The simulations were performed using the Monte

Carlo Simulation approach. The main reason behind

using this algorithm is its simplicity in dealing with

nonhomogeneous and underlying random variables

as well as multiple simultaneous conditions, e.g.,

channel fading, interference, etc. As requested by Sea

Quest, we were able to achieve this using Monte

Carlo Simulation, which is a process of producing

large numbers of "random walks" or trial and error

calculations that are repeated many times.

2.2 Steps of the Algorithm

2.2.1 Defined Parameters and Initialization:

• We then specified simulation parameters

like source power, noise power, relay

distances and fading models. Similarly,

the eavesdropper interference level and

relay node density were initialized as

well. This framework guaranteed that all

premises for the simulations were

defined correctly.

2.2.2 Generated Random Samples:

• Example of some random samples for

the channel fading severity, the

interference levels and other related

variables. These samples showed

various network conditions and

scenarios.

2.2.3 Calculated Performance Metrics:

• For each sample, the performance

metrics were calculated:

o Secrecy Capacity: Utilized the

Shannon-Hartley theorem to

determine secrecy capacity,

considering the impact of

interference and channel fading.

o Energy Efficiency: Evaluated by

comparing the achievable rate to

total power consumption,

incorporating both fixed and

adaptive power allocation

strategies.

o Latency: Measured by simulating

the impact of security protocols on

communication delays,

considering encryption strength

and channel coding.

2.2.4 Applied Multi-hop Strategies:

• Conclusion: Different types of multi-hop

relay strategies are studied to investigate

their effects on the security rate and

efficiency. After, we simulated the

interference to deal with interface

management tools like beamforming and

android alignment.

2.2.5 Analyzed Trade-offs:

• Poured over the trade-offs between

secrecy and speed It required plotting

curves and heatmaps showing how net

configurations or conditions changed

results.

2.2.6 Generated Graphs and Visualizations:

• The obtained results were exploited

based on accurate calculated metrics to

create plots such as secrecy capacity

versus SNR, energy efficiency vs relay

density and spectrograms of channel

fading. These graphics help to render the

results of the simulation in a clear way.

2.2.7 Compared with State-of-the-Art:

• The simulation results were compared

with existing literature and state-of-the-

art approaches to validate the findings

and highlight improvements or

deviations.

2.3 Why the Algorithm Was Used

We selected Monte Carlo Simulation because of

something it is good at: modeling complex and

stochastic systems with multiple variables in many

possible scenarios. This technique ensures that a wide

Security and Efﬁciency Trade-Offs in Mixed Cooperative Relay Systems with Eavesdropper Interference

653

variety of outcomes with their respective probabilities

are considered to perform rigorous and realistic

analysis on the performance evaluation for networks

under various scenarios. As the Monte Carlo

Simulation is flexible and easy to use as well as

accurate, it is a good resource for evaluating security-

efficiency trade-offs in relay networks especially

when random numbers appear such as fading or

interference.

3 IMPLEMENTATION

Figure 1: Showing Secrecy Capacity vs SNR for Different

Relay Strategies

Figure 2: Showing Secrecy Outage Probability vs

Eavesdropper Density

Figure 3: Showing Energy Efficiency vs Relay Node

Density

Figure 4: Showing Secrecy Capacity vs Relay Position for

Various Eavesdropper Locations

Figure 5: Showing Impact of Adaptive Relay Selection on

Secrecy and Efficiency

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Figure 6: Showing Secrecy vs Efficiency Trade-off Curve

Figure 7: Showing Probability of Secure Connectivity vs

Relay Transmission Power

Figure 8: Showing Latency vs Secure Level under

Eavesdropper Interference

Figure 9: Showing Impact of Channel Fading on Secrecy

and Efficiency

Figure 10: Showing Heatmap of Secrecy Capacity vs

Channel Fading Severity and Interference Level

The series of simulations conducted focus on

evaluating various performance metrics in relay

networks under different conditions, particularly

emphasizing security and efficiency. The models and

graphs presented provide insightful analysis into the

interplay between secrecy capacity, interference,

channel fading, and relay strategies.

3.1 Simulation Overview

1. Secrecy Capacity vs. Signal-to-Noise

Ratio (SNR): The simulation shows how

secrecy capacity changes as SNR increases

under different relay strategy (D-F vs. A-F)

While there are eavesdroppers not to

mention original source destination channel;

Security and Efﬁciency Trade-Offs in Mixed Cooperative Relay Systems with Eavesdropper Interference

655

This points out that, secrecy capacity grows

monotonic with the increment of SNR and

eavesdropper interference presents different

impacts on each strategy by selecting a relay

tuned to both the concerned metric secret

capacities (Fig1, Fig2).

2. Secrecy Outage Probability vs.

Eavesdropper Density: the secrecy outage

probability versus eavesdropper density is

demonstrated by a graphic, which similarly

validates that inactivation of nodes would

result in more secrecy outages induced from

increase in eavesdroppers’ densities. It

highlights the need to optimize relay

placement and density management for

network security to be preserved (Fig 3, fig

4).

3. Energy Efficiency vs. Relay Node Density:

As will be shown in the ensuing analysis,

here it exposes trade-off between relay

density and energy efficiency. The first part

compares fixed and adaptive power

allocation for different regions of SNR,

showing that while the increase in relay

density results in greater security it also

degrades energy efficiency (Fig 5, Fig 6).

4. Secrecy Capacity vs. Relay Position: This

simulation evaluates how the position of

relay nodes affects secrecy capacity,

offering insights into optimal relay

placement relative to the eavesdropper to

maximize secrecy (fig 6).

5. Impact of Adaptive Relay Selection on

Secrecy and Efficiency: This simulation

illustrates that adaptive strategies can

achieve properties of static and dynamic

relay selection algorithms alongside greater

secrecy capacity and performance, over a

wide range condition. By contrast fixed

relaying is worse compared with either

approach in most cases (Fig 7).

6. Secrecy vs. Efficiency Trade-off Curve:

This curve illustrates the tradeoff between

secrecy and efficiency by demonstrating

how different network configurations in

conjunction with varying levels of

eavesdropper interference affect this balance

(Fig 6).

7. Probability of Secure Connectivity vs.

Relay Transmission Power: This plot

investigates how relay transmission power

impacts secure connectivity probability,

considering different eavesdropper

capabilities. It underscores the need for

careful power management to enhance

secure connectivity while managing

efficiency (Fig 8).

8. Latency vs. Security Level: This

simulation explores the impact of security

protocols on latency, showing how stronger

security measures like encryption and

channel coding affect real-time

performance, especially under eavesdropper

interference (Fig 8).

9. Impact of Channel Fading on Secrecy and

Efficiency: The dual axis heatmap evaluates

the effects of various fading models

(Rayleigh, Nakagami, Rician) on secrecy

capacity and efficiency, revealing how

different fading scenarios impact network

performance (Fig 9).

10. Interference vs. Secrecy Capacity in

Multi-hop Relay Networks: This

simulation assesses how multi-hop relay

strategies and interference management

techniques influence secrecy capacity,

demonstrating the effectiveness of advanced

strategies like beamforming (Fig 10).

3.2 Advantages and Effectiveness

The implemented models offer a comprehensive view

of how different factors affect relay network

performance. By including multiple metrics (secrecy

capacity, energy efficiency, latency) and scenarios

(various fading models, interference levels), these

simulations provide a robust analysis of network

dynamics.

3.2.1 Advantages:

• Detailed Insights: Each simulation targets

specific aspects of network performance,

offering detailed insights into trade-offs and

optimal strategies.

• Adaptive and Static Comparisons: The

comparison between adaptive and static

relay strategies highlights the practical

benefits of dynamic approaches.

• Comprehensive Coverage: By addressing

both security and efficiency, simulations

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help in understanding the holistic impact of

network design decisions.

Comparison with State-of-the-Art: Most of the

state-of-the-art approaches, however, support only

isolated metrics or are reduced to minimal scopes. On

the other hand, we focus on multi-dimensional

analysis which includes fading, interference and relay

strategies. By shedding light on the "all sides" of

performance trade-offs, this holistic view

significantly increases the leverages to design and

optimize real network.

4 RESULTS

The quantitative results from the simulations provide

some valuable information on these performances of

relay networks under different scenarios. For secrecy

capacity vs signal-to-noise ratio (SNR) we show that

improved SNR leads to increased secrecy capacity,

but the gain depends on an appropriate choice of relay

strategy and presence of eavesdroppers. It is observed

that in general decode-and-forward strategies seem to

have an advantage over amplify-and-forward with

respect to secrecy capacity under same conditions.

The impact of the eavesdrop density on the peak

secrecy outage probability is studied in Fig., which

clearly shows that as we increase the optimal relay

placement and thus reduce their efficient number to

mitigate security issues are not be overlooked provide

fire quality services. An accessible representation of

this trade-off is the impact relay node density has on

energy efficiency where a direct gain in security

comes with downtime from an energy-efficient

perspective, significantly significant when fixed

power allocation strategies are contrasted to adapt.

Simulation results for performance of Latency versus

security level. Increased latency is observed with

more secure communication, e.g., encryption and

manifests in high-active-eavesdropper-interference

cases (A). Protecting the secrecy capacity and

efficiency of wireless communication network

against channel fading effects from eavesdroppers

can be clearly observed through heatmap analysis for

different bad fading models. Next, the interference vs

secrecy capacity comparison in multi-hop relay

networks reveals the benefit of powerful

(beamforming) and state-of-the-art techniques to

manage significant level of interferences for

improving overall network security.

5 CONCLUSIONS

These simulations give a good overall impression of

how various aspects affect relay network

performance. Highlights: Trade-off between secrecy

capacity and signal-to-noise ratio effective; decode-

and-forward strategies usually better than amplify-

and-forward Addition of more eavesdroppers

increases the secrecy outage probability, making it

necessary to find an optimal positioning for relay

node. Initial energy efficiency versus relay density

analysis reveals inherent trade-offs, and latency

results suggest that more secure protocols could raise

latencies in particular under active eavesdropped

interference. The heatmap on channel fading effects

has different impacts regarding secrecy capacity and

efficiency, while the investigation of multiple-hop

interference demonstrates the success of

beamforming or advanced management techniques.

Such results help in designing safe and high-speed

relay networks.

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