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Speed5G Workshop London presentation of the Speed5G RRM framework


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Presentation of the Speed5G RRM framework at the Speed5G Workshop on March 7th 2018 in London

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Speed5G Workshop London presentation of the Speed5G RRM framework

  1. 1. Presenter: Andreas Georgakopoulos SPEED-5G Workshop Advanced Spectrum Management in 5G+ Networks London, 7th March 2018 RRM Innovations
  2. 2. Outline 4 RRM Algorithms Overview 4 Spectrum and Radio Resource Management in a 5G Context 4 Hierarchical Radio Resource Management ➨ Increased capacity and scalability ➨ Increased stability 4 hRRM Architectural Overview 4 hRRM Component Description 4 Spectrum Band Capability Learning 4 Learning Algorithm Overview 4 Machine Learning Powered MADM 4 Indicative Results 4 Conclusions 72
  3. 3. RRM Algorithms Overview 73
  4. 4. Spectrum and Radio Resource Management in a 5G Context 4 Traditionally frequency allocation is decided centrally. 4 In a 5G architecture there are expected to be many small cells (up to the order of hundreds) between the underlying macro cells. 4 Problem: Current schemes are not scalable to such an increased number of cells. 4 Solution: Decentralization is a solution; a distributed approach is proposed. 74
  5. 5. Hierarchical Radio Resource Management 75 Centralized management Input: • Users • Traffic • Radio conditions • Mobility Output: • Decisions on frequency, channels used (operation framework per cell) Algorithms Distributed management Distributed management 4 Develop hierarchical (blending distributed and centralised) management of ultra-dense multi-RAT and multiband networks It will enable: 4 Capacity à Through small cells and efficient resource allocation 4 Scalability à Through distributed management, in coordination with centralized schemes 4 Stability à Through machine learning Problem: Centralised management can not scale as much as networks will scale in 5G era
  6. 6. Increased Capacity and Scalability 4 The introduction of new small cells and new bands will add more capacity to the network 4 Improve scalability through distributed management ➨ As more small cells per macro are added, centralized decision making would need more data from each new cell (centralized management) ➨ Distributed management: Intelligent nodes can handle decision making in a more local basis, hence related signaling will be limited (compared to centralized) 76 Signaling traffic decreases due to the use of distributed management (analytical simulation results)
  7. 7. Increased Stability 4 Traffic T fluctuates in time t 4 Traffic levels can be used for certain timeframes in order to have more stable conditions 4 Machine-learning algorithms can be used 77 Trafficlevelsas definedin GreenTouch A. Georgakopoulos, A. Margaris, K. Tsagkaris and P. Demestichas, "Resource Sharing in 5G Contexts: Achieving Sustainability with Energy and Resource Efficiency," in IEEE Vehicular Technology Magazine, vol. 11, no. 1, pp. 40-49, March 2016 Learned conditions T0 in t0 T1 in t1 T2 in t2 T3 in t3 T4 in t4 Time of the day
  8. 8. hRRM Architecture Overview 4 Our architecture consists of entities for distributed spectrum and radio resource management (dRRM), as well as the traditional centralized mechanisms (cRRM). 4 The framework has the ability to switch between the centralized and distributed operational modes or combine them in an hierarchical approach. 78 cRRM dRRM1 dRRM3dRRM2 Cell 1 Cell 2 Cell 3 hRRM
  9. 9. hRRM Component Description 4 dRRMs: Use game theory and learning functionality to predict next channel states and reach a decision on which channels to use. Instances run independently on each cell and no communication is required. 4 cRRM: Coordinates dRRMs, speed ups adaptation to acute network or traffic changes and ensures fairness. It keeps track of channel usage and intervenes in cases when it can preempt conflicts. 79
  10. 10. Spectrum Band Capability Learning 4 During cell operation, performance attributes are measured. 4 Each cell chooses which attributes to take into consideration and their respective weights. ➨ One proposed configuration uses Bit Rate average value and variance, to access performance as well as stability. For example, an eMBB use case would prioritize average BR, whereas a URLLC use case would focus on avoiding instability. 4 A Channel Appropriateness Value (CAV) is calculated using the achieved values of each attribute, as well as their expected best and worst values. 80
  11. 11. Learning Algorithm Overview 81 Is knowledge sufficient? Local channel status knowledge Random selection of unknown channel Machine Learning Powered MADM selection Found available channel in LTE band? Update of knowledge YES YES NO NO 4 Selection is based on previous knowledge regarding local channel status and network capabilities, obtained through learning functionality. 4 When knowledge is not sufficient or recent enough an unknown channel is randomly chosen. 4 Random selection is also invoked with a probability dependent on the latest achieved channel appropriateness value (CAV). Use different technology (e.g. Wi-Fi) or Invoke cRRM
  12. 12. Machine Learning Powered Multi Attribute Decision Making (MADM) 4 Knowledge is formed by measuring chosen attributes of the used channel. 4 Each time a channel is used, the probability of acquiring a certain value for each attribute is updated. Based on these probabilities, an Estimation of the Channel State (ECS) is produced. 4 Each possible decision is evaluated with the Channel Appropriateness Value (CAV) associated with the estimated attribute values. 4 ECS and CAV are combined to calculate a priority value for each channel. 4 The channel with the maximum priority is selected. 82 Collection of measurements Calculation of channel priorities based on probabilities and associated appropriateness value Selection of channel with maximum priority value Update of probabilities Calculation of appropriateness value for each channel
  13. 13. Indicative Results 4 The hRRM algorithm is being tested in terms of convergence capability, speed and adaptability with varying parameters. 4 In the Fig. we can see the progressive convergence of CAV to 1.0 after algorithm iterations, assuming 30 cells simultaneously initiated with zero knowledge. 83
  14. 14. Indicative Results 4 The Fig. illustrates an example of how a specific cell produces an estimation of the spectrum channel capabilities and the corresponding Channel Appropriateness Value, based on knowledge from prior selections. 84
  15. 15. Conclusions 4 Capacity ➨ Introduction of new small cells and new bands ➨ Efficient spectrum and radio resource allocation 4 Scalability ➨ Intelligent nodes handling decision making in a local basis ➨ Distributed, learning-based selection ➨ Limited signaling 4 Stability ➨ Adaptation to network or traffic changes ➨ Inter-Cell fairness 85
  16. 16. Thank you for your attention! 86 Acknowledgment: The research conducted by Speed-5G receives funding from the European Commission H2020 programme under Grant Agreement N : 671705. The European Commission has no responsibility for the content of this presentation. Find us at