1 Radio Management Approach in LTE Networks Saba Al-Rubaye BSC. MSC. PhD. CEng. SMIEEE

2 Current Research & Expertise 2 LTE Radio Systems Smart Grid Communications Energy Efficiency Management Wireless Communications

3 Agenda - Selected Research:  Senario1: Cognitive Radio for Emergency Networks  Senario2: Spectrum Resource Management  Senario3: Power Management  Senario4: Mobility Management - On going research

4 Global Mobile Broadband Traffic Source Cisco VNI, 2010  Global mobile data traffic will increase 39 times from 2009 to 2014.  According to the Cisco forecasting, by 2014, annual global mobile data traffic will reach 3.5 exabytes per month. 4 What is the problem?

5 Industries are shifted from 4G networks to 5G networks Optimization and Self-Network by improve the efficiency of transmitted information to achieve QoS using (Management Networks, and Cooperative RAN’s) 5

6 Radio Management Network 6

7 Future of Wireless Architecture A network that consists of a mix of macro cells and low-power nodes, e.g. Pico, Femto, Relay Node Heterogeneous networks include LTE-Long Term Evolution and Small Cell Architecture enables various opportunity of accesses in the next generation cellular systems. 7

8 8 Cognitive Radio for Emergency Networks Senario1

9 Cognitive Radio for Emergency Networks Software defined radios –SDR system replaces the traditional hardware components with software programmable systems like FPGA. Cognitive Radio – how the radio behaves Sense the spectral environment over a wide bandwidth Adapt power levels and transmission bandwidths to avoid interference to any primary user Learn the best times and locations to coexist with primary users CR should 9 R.7 & 15

10 10 CRoF Networks for Emergency Communications  CRoF (Cognitive Radio over Fibre) used to overcome interruption in services and communications between the cognitive cells.

11 CRoF for Microcells Applications CRoF system can be applied to serve cognitive communication at microcells levels.

12 Cognitive Femtocell: Future Wireless Networks System Model : developed gateway broadband router based cross layer opportunistic management is used to control the data packet delivery between the macrocell and the cognitive femtocell using a Priority Queuing (PQ) strategy. Aim and Objective Enhance throughputs and coverage Relieve load of the macro network Low cost Reduce power consumption 12 R. 3

13 13 Spectrum Resource Management Scenario2

14 System Model for Spectrum Coordination The new spectrum management system has Several Functional Modules The management entity exchange the information SD decided when the channel should be accessed to avoid overlapping The Sensing Algorithm aims to determine the presence or absence of PUs on a channel based on energy detection (SNR, interference) 14

15 Each spectrum band is characterised for the spectrum decision based on local observations of CR. Spectrum Access Decision Novel algorithm for Spectrum Decision Sequence Else Spectrum Decision Sequence 15

16 16 The 802.11e standard is used with the enables Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) based MAC protocol. CSMA/CA has the ability to listen before talk to sense PU, and the advantage to avoid overlapping in CR system Using CSMA/CA Protocol

17 17 Cognitive LTE and Femtocell Systems Testbed in OPNET

18 Increase in throughput with the increase in numbers of users It means the proposed system is able to nearly utilize all the available bandwidth The proposed system adaptively allocates a proper bandwidth to each traffic flow. Therefore, the traffic patterns with larger white space achieve higher throughput and lower time delay Results 18

19 19 Power Management Scenario3

20 Power Management and Optimization 20 Objective : Reduce the management complexity and enabling seamless service provision by maximising power efficiency using new adaptive techniques R. 10

21 21 The transmit power of each femtocell per subcarrier is : : Received power from the macrocell : Antenna gain in direction of the femtocell : Path loss between the macrocell BS and the femtocell r : is the distance between them

22 Auto- Configuration Adjustment The algorithm periodically updates the pilot power configuration based on each femtocells (RSS) received signal strength and global traffic delivery in the network. Main algorithm (Transmission pipeline MAC) Update of Transmission power Traditional power is fixed 22

23 Power received in Watts as a function of time in seconds where the femtocell users are deployed randomly in a macrocell. 23 Results

24 24 Mobility Management Senario4

25 The deployment of small cell BS increase the numbers of unnecessary handovers for mobile users moving between different domains which lead to reduce the resource availability. Mobility Management 25 R. 9

26 Unnecessary Handover Crises Proposed Solution  Novel Call Admission Control (CAC) is proposed to the IP layer to allocate mobile users according to their speeds. 26

27 Probability of Handover Handover occurred in LTE and Small Cell networks can be calculated as follows: Total number of handovers that occurred at specific state Probability of being in a state Number of services residing at a certain domain 27

28 Novel CAC Mechanism CAC is proposed to monitor the necessary measured parameters on the cell and control the admission of incoming connections 28 from the UE The handover threshold may be adjusted according to the QoS requirement, and the velocity of the user

29 29 SYSTEM MODEL The handover call arriving rate in diverse small cell environments is hard to handle because of the frequent and unwanted handovers that causes significant decrease in the QoS. The queuing system with Poisson Distribution Process with parameter of product of λ input is considered in this work Multi server queues systems Poisson of rates

30 30 If the value of K less than N denotes that the system gives more priority to handover calls from macrocell to femtocell When the values of K and N very close, it means the femtocell coverage area with a lower probability of handover call rate Markov chain model of handover scheme between LTE and Femtocell Call Arrival Predicting Markov Chain Model

31 System Implementation  A new code has developed to allow calculating the ground speed of mobile users and attach it to the packets attributes to enable the functioning of the proposed CAC. Code to decided to connect to arrival UE or not Node Model where CAC integrated 31

32 Simulation Scenarios The aim of these five case studies is to evaluate the different approaches and technologies that can be used to reduce the numbers of handovers for mobile phones travelling between LTE and small cell transmission domains. Parameters Value LTE transmit power40dBm Small cell BS transmit power20dBm Number of LTE5 Number of Small Cell5 Number of UE3 32

33 The comparison with the traditional scheme shows that the algorithm proposed have a better performance in the rate of less unnecessary handovers Results 33

34 This scenario will include 5G LTE to support smart grid network operating system in order to provide utilities with the highest performance network and most competitive access costs. Scenario for Investigation Fibre Heterogeneous Wireless 34

35 Thank you