# Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems

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## Abstract

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## 1. Introduction

- We design an energy-efficient resource allocation framework and formulate a non-convex MINLP problem for joint optimization of user selection, subchannel allocation, user selection, power allocation, and the number of activated RRUs in order to enhance the energy efficiency in dynamic large-scale 6G-IoT ecosystems.
- In order to decompose the problems of non-convex optimization into small segments, we leverage the fractional programming property. We propose the Lagrangian decomposition method to optimize power allocation and the KM algorithm to dynamically allocate resources to IoT users to obtain optimal solutions. This can significantly reduce the computational complexity and make the optimization process more scalable in dynamic large-scale IoT systems.
- The effectiveness of the proposed algorithm compared to the leading-edge approaches in the form of energy efficiency gain is verified through extensive simulations.

## 2. IoT Network Model

#### 2.1. Channel Model and Estimation

#### 2.2. Data Transmission Model

#### 2.3. The Power Consumption Model

## 3. Resource Allocation and Optimization Problem

#### 3.1. Energy Efficiency Optimization

#### 3.2. Formulation of Optimization Problem

#### 3.3. Novel Dynamic Resource Allocation Design

#### Transformation of Energy Efficiency Optimization

**Theorem**

**1.**

**Proof.**

## 4. Proposed Joint Optimal Iterative Method

#### 4.1. Relaxed Problem Formulation

#### 4.2. Dual Decomposition Problem

#### 4.3. Inner Loop Method

#### 4.3.1. Optimal Power Allocation

#### 4.3.2. Optimal Number of Activated RRUs Allocation

#### 4.3.3. Optimal Subchannel Allocation

#### 4.4. Outer Loop: Master Subproblem Solution

#### 4.5. Optimal User Selection

- Initialize perfect matching M and feasible labelling ℓ in $\ell \left(v\right)$.
- Set $S=\left\{v\right\},T=\theta $.
- If ℜ denotes an optimal matching of complete bipartite graph G, the Equation (29) can be optimally solved.
- Otherwise, select vertex $v\in X$.
- If $T={H}_{\ell}\left(S\right)\ne Y$ and $S\subseteq X$, then set ${H}_{\ell}\left(S\right)=T$.
- Update the feasible labels as$${\beta}_{\ell}=\underset{s\in S,y\notin T}{min}\left\{\ell \left(x\right)+\ell \left(y\right)-{w}_{t}\left(x,y\right)\right\}$$$${\ell}^{\prime}\left(v\right)=\left\{\begin{array}{c}\ell \left(v\right)-{\beta}_{\ell}\phantom{\rule{0.277778em}{0ex}}\mathrm{if}\phantom{\rule{0.277778em}{0ex}}v\in S\hfill \\ \ell \left(v\right)+{\beta}_{\ell}\phantom{\rule{0.277778em}{0ex}}\mathrm{if}\phantom{\rule{0.277778em}{0ex}}v\in T\hfill \\ \ell \left(v\right),\phantom{\rule{4pt}{0ex}}\phantom{\rule{4pt}{0ex}}\mathrm{otherwise}.\hfill \end{array}\right.$$
- If ${H}_{\ell}\left(S\right)\ne T$, set $y\in {H}_{\ell}\left(S\right)-T$, and go to step 2.

#### 4.6. Proposed Joint Resource Allocation Algorithm

Algorithm 1: Proposed JEERA Algorithm to Maximize Energy Efficiency |

#### 4.7. Computational Complexity and Feasibility

## 5. Performance Evaluation and Discussion

#### 5.1. Numerical Results and Discussion

#### 5.1.1. Effects of Transmission Power on Energy Efficiency

#### 5.1.2. Impact of Transmit Power on Average System Throughput

#### 5.1.3. Effects of Transmit Power on Total Power Consumption

#### 5.1.4. Effect of IoT Devices on Energy Efficiency

#### 5.1.5. The Convergence of Proposed Iterative Algorithm

#### 5.1.6. Impact of SINR Constraints on the Performance of Energy Efficiency

#### 5.1.7. Effects of Activated RRUs on Transmission Power

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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Parameter | Values |
---|---|

Operating frequency | 3.8 GHz |

Total channel bandwidth | 8 MHz |

Transmitting antenna gain | 12 dB |

Path-loss exponent | 4 |

Constant back-off factor | 0.3 |

Noise power per subchannel | −167 dBm |

Power amplifier efficiency | 0.2 |

Number of subchannels | 32 |

Power consumption | 50 dBm |

Minimum data rate | 4.2 Mbps |

SINR threshold | 2.0 dB |

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**MDPI and ACS Style**

Ansere, J.A.; Kamal, M.; Khan, I.A.; Aman, M.N.
Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems. *Sensors* **2023**, *23*, 4711.
https://doi.org/10.3390/s23104711

**AMA Style**

Ansere JA, Kamal M, Khan IA, Aman MN.
Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems. *Sensors*. 2023; 23(10):4711.
https://doi.org/10.3390/s23104711

**Chicago/Turabian Style**

Ansere, James Adu, Mohsin Kamal, Izaz Ahmad Khan, and Muhammad Naveed Aman.
2023. "Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems" *Sensors* 23, no. 10: 4711.
https://doi.org/10.3390/s23104711