Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks
1.1. Related Work
- Sensitive mode of operation: Typical ES SON algorithms are susceptible to reaction that achieves ES at the expense of QoS after an event has been completed. Given the well-populated city dynamics with bus passenger ridership in relation to deployed cellular environment, by the time SCs overloading or underloading is detected and a realistic algorithm is opted to solve the known issue, the conditions may already change . In the 5G environment, this problem can further escalate when disparate passenger ridership and plethora of cell types responsible to support smart city eco-system are not in harmony.
- SCs wake up time: Sleeping SCs require a specific amount of time to wake up . Any passenger entering a SC footprint that is still in a sleeping state would add high latency experience. Thus, there is a need to modernise conventional paradigms pro-actively to maintain low latency requirements of 5G in a more agile fashion, i.e., pro-active ES by passenger’s mobility management.
- User Association to sleeping SCs: A key challenge in the HetNet cell On–Off switching strategy is to establish user associations (bus passengers ridership association) to the correct serving SCs that are switched ON while passengers are within its coverage footprint , thus contributing to overhead challenges. Existing ES schemes have not apparently provided evidence to address this challenge where 5G QoS demands low-overhead, low-costs and highly efficient architectures.
- SON upright design: Conventional ES solutions when implemented together in a HetNet environment are susceptible to conflicts  that require intelligence to resolve. SON use-cases that are liable to be conflicted are: traffic offloading while SC switching [3,13] and prediction of passengers to neighbouring cells . For the first conflict, Cell Individual Offsets (CIOs) along with transmission power settings play a major role, whereas a correct distancing metric for the classification of mobility predictions is used for the second one. Furthermore, traffic offloading through vertical, horizontal or both is an important method when BS transmit power is concerned . In horizontal offloading, SCs have low transmit powers within the certain cell range to offload the traffic of neighbouring cells.Therefore, between SCs, horizontal offloading cannot always be realised. Consequently, vertical offloading often becomes the only choice for some SCs to go into sleep mode if its neighbouring SCs are not in the proximity.
1.2. Contributions Organisation
- As a building block of novel Energy-Aware Framework, we develop a spatiotemporal mobility prediction framework by analysing a statistical K-Nearest Neighbour (KNN) model which would modernise ES conventional limitations.
- A novel method of passengers future location estimation is proposed to map the next cell spatiotemporal Handover (HO) based on the idea of landmarks using multiple K values in KNN model and a detailed comparison.
- Another novelty of this proposal is that, based on the future cell load information and CIOs as optimisation variables for load balancing among SCs, a proactive ES optimisation problem is formulated to reduce power and energy consumption by switching off lightly loaded, idle or underutilised HetNet SCs. Intelligence in load balancing would exploit specifically lightly loaded SCs to be switched off while satisfying QoS.
- Based on the information achieved from mobility management of passengers ridership and ES awareness, a novel scheme for CO2 reductions is also quantified.
2. System Model
- Statistical KNN-based Passengers Mobility Prediction
- Passengers Future Location Estimation
- Proactive-Energy Saving Optimisation based on CO2 Reduction
2.1. Energy-Aware Framework
2.2. Statistical KNN-Based Passengers Mobility Prediction
2.3. Passengers Future Location Estimation
|Algorithm 1: Deep channel residual learning|
2.4. Proactive-Energy Saving Optimisation Based CO2 Reduction
3. Proposed Approach
3.1. Machine Learning (ML) Driven Classification Accuracy
3.2. Reinforcement Learning (RL) Driven Energy Savings
4. Performance Evaluation
4.1. Classification Prediction Accuracy
4.2. Energy Saving, Benchmarking and Metrics
Conflicts of Interest
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|Number of BS||10 (1 MC and 9 SCs)|
|Physical Resource Blocks (PRBs)||100|
|Number of iterations for RL||100|
|Number of iterations for ML||100|
|Total bus routes||673|
|Total number of passengers (Peak)||0.5 M|
|Total number of passenger (Off-Peak)||0.2 M|
|Number of classes||2|
|Area of passenger movement probability||100%|
|Total simulation duration||21 h|
|Machine Learning Algorithm||Accuracy||Precision||Recall||F-Measure|
|K-Nearest Neighbour (KNN)||98.82%||0.97||0.96||0.97|
|Discriminant Analysis (DA)||98.75%||0.96||0.96||0.96|
|Support Vector Machine (SVM)||98.75%||0.97||0.95||0.95|
|Decision Tree (DT)||97.78%||0.97||0.96||0.97|
|Naive Bayes (NB)||86.94%||0.86||0.85||0.86|
|Artificial Neural Network (ANN)||73.08%||0.73||0.72||0.73|
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Asad, S.M.; Ansari, S.; Ozturk, M.; Rais, R.N.B.; Dashtipour, K.; Hussain, S.; Abbasi, Q.H.; Imran, M.A. Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks. Signals 2020, 1, 170-187. https://doi.org/10.3390/signals1020010
Asad SM, Ansari S, Ozturk M, Rais RNB, Dashtipour K, Hussain S, Abbasi QH, Imran MA. Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks. Signals. 2020; 1(2):170-187. https://doi.org/10.3390/signals1020010Chicago/Turabian Style
Asad, Syed Muhammad, Shuja Ansari, Metin Ozturk, Rao Naveed Bin Rais, Kia Dashtipour, Sajjad Hussain, Qammer H. Abbasi, and Muhammad Ali Imran. 2020. "Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks" Signals 1, no. 2: 170-187. https://doi.org/10.3390/signals1020010