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Energy Efficiency

ASecurity

Evaluates the trade-off between energy efficiency and physical-layer security in an IRS-assisted MISO network with cooperative jamming. Probes how varying transmit power constraints and secrecy rate thresholds impact system performance compared to baseline beamforming strategies. Use when the user has predictions and gold and needs to compute Energy Efficiency.

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  • Added September 11, 2026
researchpythongonodegitsecurityperformance

Security analysis

A100/100

Scanned September 11, 2026

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SKILL.md
---
name: energy-efficiency
description: Evaluates the trade-off between energy efficiency and physical-layer security in an IRS-assisted MISO network with cooperative jamming. Probes how varying transmit power constraints and secrecy rate thresholds impact system performance compared to baseline beamforming strategies. Use when the user has predictions and gold and needs to compute Energy Efficiency.
metadata:
  skill_kind: metric
  source_arxiv: 1911.05133
  bibtex_key: wang2019energy
  confidence: high
---

# energy-efficiency

> Energy-Efficient Beamforming and Cooperative Jamming in IRS-Assisted MISO Networks — Wang et al. (2019) (arXiv:1911.05133, 2019)

## What this evaluates

Evaluates the trade-off between energy efficiency and physical-layer security in an IRS-assisted MISO network with cooperative jamming. Probes how varying transmit power constraints and secrecy rate thresholds impact system performance compared to baseline beamforming strategies.

## Datasets

- (no dataset; pure metric skill)

## Metrics

- `Energy Efficiency` **(primary)** — range: bits/Hz/Watt
  - Ratio of achievable secrecy rate to total power consumption (transmit power plus circuit power). Maximized under minimum secrecy rate constraints.
- `Achievable Secrecy Rate` — range: bits/Hz/s
  - Maximum difference between the legitimate receiver's channel capacity and the worst-case eavesdropper's capacity, subject to power and phase-shift constraints.

## Input / output format

**Input**: Simulation parameters including node coordinates, path loss exponents, channel fading coefficients, maximum transmit power (P_max), secrecy rate threshold (R_th), bandwidth, and circuit power values.

**Output**: Numerical values or plots of Energy Efficiency and Achievable Secrecy Rate across varying P_max and R_th.

## Scoring recipe

```python
def compute_metrics(P_max, R_th, channel_params, power_params):
    C_legit = compute_capacity(channel_params['B_U'], P_max)
    C_eve = max([compute_capacity(channel_params[f'B_E_{k}'], P_max) for k in K])
    secrecy_rate = max(0, C_legit - C_eve)
    total_power = P_max + power_params['circuit_power']
    ee = secrecy_rate / total_power
    return ee, secrecy_rate
```

## Common pitfalls

- Energy efficiency peaks and then decreases as transmit power increases because power consumption grows faster than secrecy rate gains.
- Feasibility breakpoints appear in benchmarks when R_th exceeds the maximum achievable rate even at full power.
- Benchmarks like 'Rate-IRS' maximize secrecy rate without considering power costs, yielding high rates but poor energy efficiency.

## Evidence (verbatim from paper)

> Fig. 2 shows the energy efficiency versus the maximum transmit power achieved by different designs. The minimum secrecy rate threshold is set as  $R_{th} = 0.5$  bits/Hz/s. It can be seen that the energy efficiency achieved by the proposed method is the best among all the benchmark schemes.

## Citation

```bibtex
@misc{wang2019energy,
  title={Energy-Efficient Beamforming and Cooperative Jamming in IRS-Assisted MISO Networks},
  author={Wang et al. (2019)},
  year={2019},
  note={arXiv:1911.05133}
}
```

- arXiv: 1911.05133

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