# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques

^{*}

## Abstract

**:**

## 1. Introduction

- Performance comparison of classic FFR-based ICIC algorithms that take into account the number of users and their distributions along the cell (edge and center);
- Statistical analysis of the strict frequency reuse scheme that identifies which parameters do not need to be dynamically controlled;
- Analysis of the strict frequency reuse algorithm’s performance in a mixed scenario with hotspot and homogeneous user distribution;
- Q-Learning algorithm that continually operates in the network to dynamically mitigate the performance loss (SINR) that results from the appearance of hotspots (densely populated areas).

## 2. Interference in Hotspot Scenarios

#### 2.1. Classic Solutions for Interference in Hotspot Scenarios

#### 2.1.1. MIMO Systems

#### 2.1.2. Small Cells

## 3. Fractional Frequency Reuse

#### 3.1. ICIC Techniques

#### 3.1.1. Strict Frequency Reuse

#### 3.1.2. Soft Frequency Reuse

#### 3.1.3. Soft Fractional Frequency Reuse

## 4. ICIC in 3GPP Standards

## 5. Related Works

## 6. System Model

#### 6.1. Simulation Software

#### 6.2. LTE Module and ICIC on ns-3

#### 6.3. Q-Learning on ns-3

## 7. Preliminary Analysis A: Performance Comparison of ICIC Algorithms

#### 7.1. Evaluation Scenario

**Scenario 1:**UEs are concentrated in a 100 m radius, representing a classic hotspot scenario;**Scenario 2:**UEs are less concentrated, distributed over a 500 m radius circle.

#### 7.2. Results and Discussions

- FFR-based ICIC techniques can improve the performance of a mobile network, given the high interference suffered by its users. It is possible to enhance cell-edge performance without compromising cell-center users;
- Simply changing the user density can strongly impact on system performance, especially if the bandwidth is divided into sub-bands. The concentration or spreading of the users can result in different occupation of the sub-bands, leading to overloaded or nearly empty sub-bands;
- There is no scheme that performs best in every situation. However, the Strict FR and the SFFR schemes have a good performance in different scenarios, and they are both efficient at reducing ICI, especially for cell-edge users;
- The Strict Frequency Reuse (Strict FR) algorithm has the best compromise between performance and complexity, considering SFFR has a higher number of sub-bands, which leads to more complexity when adjusting the parameters. Therefore, the Strict FR is the object of the study presented in the following sections.

## 8. Preliminary Analysis B: Factorial Design using the Strict Frequency Reuse Algorithm

#### 8.1. Evaluation Scenario

#### 8.2. ${2}^{k}$ Factorial Design

#### 8.3. Full Factorial Design

## 9. Preliminary Analysis C: Evaluating the Hotspot Scenario

#### 9.1. Evaluation Scenario

#### 9.2. Simulation Results

## 10. Proposed Solution: Background, Implementation, and Simulation Results

#### 10.1. Machine Learning Techniques

#### 10.2. Reinforcement Learning

#### 10.3. Q-Learning

#### 10.4. The Q-Learning Implementation

- A set of available actions, $A={a}_{1},{a}_{2},...,{a}_{n}$;
- A set of possible states of the system, $S={s}_{1},{s}_{2},...,{s}_{n}$;
- A $Q(s,a)$ matrix to store the estimated rewards;
- $\alpha $ and $\gamma $.

- State 1: $SINR<24\phantom{\rule{3.33333pt}{0ex}}dB$;
- State 2: $24\phantom{\rule{3.33333pt}{0ex}}dB<SINR\le 27\phantom{\rule{3.33333pt}{0ex}}dB$;
- State 3: $27\phantom{\rule{3.33333pt}{0ex}}dB<SINR\le 29\phantom{\rule{3.33333pt}{0ex}}dB$;
- State 4: $29\phantom{\rule{3.33333pt}{0ex}}dB<SINR\le 30\phantom{\rule{3.33333pt}{0ex}}dB$;
- State 5: $30\phantom{\rule{3.33333pt}{0ex}}dB<SINR\le 32\phantom{\rule{3.33333pt}{0ex}}dB$;
- State 6: $SINR>32\phantom{\rule{3.33333pt}{0ex}}dB$.

#### 10.5. Evaluation Scenario

Algorithm 1: Pseudo-code of the proposed Q-learning algorithm. |

**Scenario 1**has 60 users distributed uniformly and 10 users on each hotspot. A total of 52 RBs are allocated for the common sub-band, and 16 RBs are allocated to the private sub-band.

**Scenario 2**has the double of users on each hotspot and 2/3 of the uniform-distributed users when compared to Scenario 1. Hence, there are 40 users distributed uniformly and 20 users on each hotspot, which results in a higher number of users in the system. A total of 28 RBs are allocated for the common sub-band, and 24 RBs are allocated to the private sub-band. Therefore, the available bandwidth for users allocated in the common sub-band is significantly smaller. Other simulation parameters are summarized in Table 10.

#### 10.6. Data Collection on ns-3

#### 10.7. Proof-of-Concept Simulation Results

## 11. Conclusions

## 12. Future Works

- Use other metrics to define the QL states and actions, such as the BandwidthDistribution. As presented in Section 10.3, these metrics can also be the combination of different variables. As a result, the algorithm is expected to improve in flexibility and efficiency when using two parameters, adapting to a broader set of scenarios. For example, the throughput could be included as part of the reward;
- Expand proposed scenarios: vary number of users, add mobility, varying number and location of hotspots (which could also appear in random locations), and test different values for BandwidthDistribution;
- Evaluate the system using different metrics, such as the relation between convergence speed, the amount of state/action pairs, or the Packet Loss Ratio (PLR);
- Apply the solution on a system without isotropic antennas, such that transmission is made in sectors within a cell;
- Apply the presented solution for 5G New Radio (NR) or the LTE UL, given that the interference in the UL has different characteristics, when compared to DL;
- Provide a similar solution, replacing the RL algorithm for, e.g., multi-armed bandit, providing a simpler solution.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Scenario with reuse factor of 3. In this topology, no adjacent cells share bandwidth, i.e., their frequency bands are disjoint.

**Figure 2.**Bandwidth distribution and power allocation for the Strict frequency reuse algorithm in a cluster of three cells.

**Figure 3.**Bandwidth distribution and power allocation for the soft frequency reuse algorithm in a cluster of three cells.

**Figure 4.**Bandwidth distribution and power allocation for the soft fractional frequency reuse algorithm in a cluster of three cells.

**Figure 5.**LTE-EPC data plane protocol stack (taken from the ns-3 documentation [40]).

**Figure 6.**Scenario for preliminary analysis a: performance comparison of ICIC algorithms. The Radius R is set to 100 m in Scenario 1 and 500 m in Scenario 2, as described in Section 7.1.

**Figure 7.**Scenario 1 from preliminary analysis A: 10th percentile throughput for concentrated users (R = 100m). Each curve represents a different ICIC algorithm.

**Figure 8.**Scenario 1 from preliminary analysis A: SINR CDF for concentrated users (R = 100 m). Each curve represents a different ICIC algorithm.

**Figure 9.**Scenario 2 from preliminary analysis A: 10th percentile throughput for less concentrated users (R = 500 m). Each curve represents a different ICIC algorithm.

**Figure 10.**Scenario 2 from preliminary analysis A: SINR CDF for less concentrated users (R = 500 m). Each curve represents a different ICIC algorithm.

**Figure 11.**Scenario for preliminary analysis B, which is similar to the scenario in Figure 6, but without the radius R, since all users are randomly distributed according to a uniform distribution.

**Figure 12.**Full factorial design using the strict frequency reuse: results for the average throughput. Each curve is a different bandwidth distribution.

**Figure 13.**Full factorial design using the strict frequency reuse: results for the 10th percentile throughput. Each curve is a different bandwidth distribution.

**Figure 14.**Full factorial design using the strict frequency reuse: results for the SINR CDF. Each curve is a different bandwidth distribution.

**Figure 15.**Scenario for preliminary analysis C with the approximate position for each hotspot, which are not randomly located.

**Figure 16.**Simulation results in terms of the 10th percentile of throughput. The dashed lines represent the scenario with hotspots.

**Figure 19.**Scenario 1 of the proposed solution: throughput results for each group of users, with and without Q-Learning.

**Figure 20.**Scenario 2 of the proposed solution: throughput results for each group of users, with and without Q-Learning.

**Table 1.**Parameters used for the comparison of the ICIC algorithms in preliminary analysis A [19].

Hard Frequency Reuse | |
---|---|

Bandwidth: cells 1 and 2 | 8 RBs |

Bandwidth: cell 3 | 9 RBs |

Strict Frequency Reuse | |

Bandwidth (center/edge) | 6 RBs each |

RsrqThreshold | 32 |

Power offset (center) | –6 dB |

Power offset (edge) | 3 dB |

Soft Frequency Reuse | |

Bandwidth (center) | 25 RBs |

Bandwidth (edge): cells 1 and 2 | 9 RBs |

Bandwidth (edge): cell 3 | 9 RBs |

RsrqThreshold | 32 |

Power offset (center) | –6 dB |

Power offset (edge) | 3 dB |

Fractional Soft Frequency Reuse | |

Bandwidth (center/edge) | 6 RBs each |

RsrqThreshold (center) | 37 |

RsrqThreshold (edge) | 32 |

Power offset (center) | –6 dB |

Power offset (middle) | –1.77 dB |

Power offset (edge) | 3 dB |

**Table 2.**Simulation parameters for preliminary analysis a: performance comparison of ICIC algorithms.

Parameter | Value |
---|---|

Bandwidth (RBs) | 25 |

UE distribution | Uniform |

Total UEs | 40 to 240 |

Cell-edge UEs | 10 to 60 |

Inter-eNBs distance | 1000 m |

Scheduling algorithm | Proportional Fair |

Channel model | Friis model |

Error model | MIESM |

UE mobility | No mobility |

Traffic model | Non-GBR TCP-based |

Video (Buffered Stream) |

**Table 3.**Simulation parameters for preliminary analysis B: parametric analysis of strict frequency reuse.

Parameter | Value |
---|---|

Bandwidth (RBs) | 25 |

UE distribution | Uniform |

Number of UEs | 80 |

Distance between eNBs (m) | 1000 |

Scheduling algorithm | Proportional Fair |

Channel model | Friss model |

Error model | MIESM |

UE mobility | No mobility |

Traffic model | Non-GBR TCP-based |

Video (Buffered Stream) |

Parameter (Average Throughput) | F0 |
---|---|

CenterPowerOffset (A) | 0.084 |

RsrqThreshold (B) | 39040.4 |

BandwidthDistribution (C) | 4301.4 |

BandwidthDistribution * CenterPowerOffset | 0.0015 |

BandwidthDistribution * RsrqThreshold | 6264.7 |

RsrqThreshold * CenterPowerOffset | 0.0012 |

A * B * C | 0.00032 |

Parameter (10th Percentile) | F0 |
---|---|

CenterPowerOffset (A) | 0.00059 |

RsrqThreshold (B) | 2688.37 |

BandwidthDistribution (C) | 6359.40 |

BandwidthDistribution * CenterPowerOffset | 0.035 |

BandwidthDistribution * RsrqThreshold | 695.91 |

RsrqThreshold * CenterPowerOffset | 0.0033 |

A * B * C | 0.00034 |

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

RsrqThreshold | 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 |

Common/Private | Bandwidth Distribution 1: 6 / 18 |

Sub-band (RBs) | Bandwidth Distribution 2: 12 / 12 |

Bandwidth Distribution 3: 18 / 6 |

Scenario 1 |
---|

60 uniformly distributed users and no hotspots |

Scenario 2 |

135 uniformly distributed users and no hotspots |

Scenario 3 |

135 users: 60 uniformly distributed users and |

5 hotspots with 15 users each |

Parameter | Value |
---|---|

Bandwidth (RBs) | 100 |

UE distribution | Uniform / Hotspots |

Number of UEs | 60 / 135 |

Distance between eNBs (m) | 1000 |

Scheduling algorithm | Proportional Fair |

Simulation duration | 6000 subframes |

Channel model | Friis Model |

Error model | MIESM |

UE mobility | No mobility |

Traffic model | Non-GBR TCP-based |

Video (Buffered Stream) |

Scenario 1 |
---|

60 users uniformly distributed |

10 users on each hotspot |

BandwidthDistribution: 52/16 |

Scenario 2 |

40 users uniformly distributed |

20 users on each hotspot |

BandwidthDistribution: 28/24 |

Parameter | Value |
---|---|

Bandwidth (RBs) | 100 |

UE distribution | Uniform / Hotspots |

Number of UEs | 110 / 140 |

Distance between eNBs (m) | 1000 |

Scheduling algorithm | Proportional Fair |

Simulation duration | 60,000 subframes |

Channel model | Friis model |

Error model | MIESM |

UE mobility | No mobility |

Traffic model | Non-GBR TCP-based |

Video (Buffered Stream) |

Interval | Scenario 1 (UEs) | Scenario 2 (UEs) |
---|---|---|

0 to 10 s | 60 + 0 on HS | 40 + 0 on HS |

10 to 20 s | 60 + 10 on HS | 40 + 20 on HS |

20 to 30 s | 60 + 20 on HS | 40 + 40 on HS |

30 to 40 s | 60 + 30 on HS | 40 + 60 on HS |

40 to 50 s | 60 + 40 on HS | 40 + 80 on HS |

50 to 60 s | 60 + 50 on HS | 40 + 100 on HS |

**Table 12.**Best and worst results for SINR and throughput of the Q-Learning solution, for each group of users.

Best SINR Gain | Worst SINR Gain | Best Tput Gain | Worst Tput Gain | |
---|---|---|---|---|

Scenario 1 | ||||

Hotspot users | 94% | 35.3% | 59.8% | 5.4% |

Outside hotspots | 99.1% | 11.1% | 6.5% | 0.2% |

All Users | 96.7% | 12.8% | 10.3% | 0.2% |

Scenario 2 | ||||

Hotspot users | 180.2% | 25.7% | 12.5% | 1.7% |

Outside hotspots | 140.9% | 8.6% | 1.9% | −3.8% |

All Users | 167.1% | 10.3% | 6.7% | −0.6% |

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Diógenes do Rego, I.; de Sousa, V.A., Jr. Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques. *Sensors* **2021**, *21*, 7899.
https://doi.org/10.3390/s21237899

**AMA Style**

Diógenes do Rego I, de Sousa VA Jr. Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques. *Sensors*. 2021; 21(23):7899.
https://doi.org/10.3390/s21237899

**Chicago/Turabian Style**

Diógenes do Rego, Iago, and Vicente A. de Sousa, Jr. 2021. "Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques" *Sensors* 21, no. 23: 7899.
https://doi.org/10.3390/s21237899