Simulation parameters Sample Clauses

Simulation parameters. The best case The worst case The average case 300 Key agreement delay (ms) 250 200 150 100 50 did not consider scenario 3, we only analyze the performance of our proposed scheme. For the simulation experiment, we set the number of initial vehicles covered by one RSU to be 20 and the velocity of all vehicles to be 20m/s. Let the key agreement delay in scenario 3 denote all necessary costs for updating the common session key. According to the key agreement algorithm for the old vehicle leaving operation, different vehicles pay different com- puting costs for the leaving operations of different vehicles. So the key agreement delay cost in scenario 3 will be computed in three cases, i.e., the best case, the worst case, and the average cost, where the best case is corresponding to First In First Out case (the first vehicle to join, the first to leave), the worst case is corresponding to First In Last Out case (the first vehicle to join, the last to leave), and the average is the mixed case. We simulate our scheme in the simulation environment in terms of the three cases. From the simulation results as shown in Fig. 11, we can see that, in the average case that is the closest to reality, the key agreement delay is still acceptable even if most of vehicles send the leaving request in a short time. We can also observe that the number of leaving vehicles affects the key agreement delay to a great extent in the worst and average cases, but not in the best case. The reason is that the increasing number of messages will bring higher procession delay in the worst and average cases, while the procession delay for the leaving event is almost constant in the best case (since RSU does not need to broadcast the public key of the branch node and the vehicle does not need to perform the scalar multiplication for updating the binary tree).

Simulation parameters. The parameters of the simulation and their associated value are defined through several elements. The prototype of these elements is InputParameter. Its physical meaning is set through ParameterQuantity. The list of possible values for this quantity is the same than for the properties. Qualifier can refine the Quantity meaning. The parameter itself has no value. It is composed of several Property elements, which also have physical meaning (within that of the parameter) and may have values. Figure 11 -­‐ InputParameter element

Simulation parameters. 82 4.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . 108 5.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . 132 5.2 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . 149 5.3 Algorithm running times 153 List of Abbreviations 3GPP Third Generation Partnership Project 4G Fourth Generation 5G Fifth Generation API Application Programming Interface BBU Baseband Unit BN Xxxxxx’s Needle BS Base Station BW Bandwidth CAPEX Capital Expenditures CAS Cell Association CbH Cost-based Heuristic CDF Cumulative Distribution Function CR Crossing Ratio C-RAN Cloud - Radio Access Network CSD Caching-Server Device CSI Channel State Information CUE Cellular User Equipment D2D Device-to-Device dB Decibel DD Disjoint Decoupled DL Downlink DUE D2D User Equipment EU European Union FD Full Duplex FIFO First-In First-Out FFR Fractional Frequency Reuse FRF Frequency Reuse Factor FTP File Transfer Protocol GA Genetic Algorithm HD Hybrid Decoupled HetNets Heterogeneous Networks HRS Heuristic Resource Slicing ICIC Inter-Cell Interference Cancellation ILP Integer Linear Programming InP Infrastructure Provider i-RRA iterative Randomized Resource Allocation ISM Industrial, Scientific and Medical ITU International Telecommunication Union JC Joint Coupled JD Joint Decoupled L3 . . . . . . . . . . . . Layer-3 LRU Least Recently Used LS-CRRM Large-Scale Cooperative Radio Resource Management LTE Long Term Evolution LTE-A Long Term Evolution - Advanced MCS Modulation Coding Scheme MINLP Mixed Integer Non Linear Programming MNO Mobile Network Operator MOCA Multi-Objective Cell Association MS Mobile Station MVNO Mobile Virtual Network Operator NC Network Coding NFV Network Function Virtualization NSI Network State Information NVS Network Virtualization Substrate ONF Open Networking Foundation OP One-point OPEX Operational Expenditures ORS Optimal Resource Slicing OTN Optical Transport Network P2P Peer-to-Peer PPP Poisson Point Process PRB Physical Resource Block QoS Quality of Service RA Resource Allocation RAN Radio Access Network RB Resource Block RRH Radio Remote Head RSS Received Signal Strength SC-FDMA Single Carrier Frequency Division Multiple Access SDN Software-Defined Networking SINR Signal-to-Interference-plus-Noise Ratio SIR Signal-to-Interference Ratio TP Two-point UE User Equipment UL Uplink V2V Vehicle-to-Vehicle VFM Virtual Function Manager VIM Virtualization Infrastructure Manager vNF virtual Network Function VoIP Voi...

Simulation parameters. Simulation Parameter Value Simulated scheme SIMO 1x2; MIMO 2x2; Transmission Mode Switching Cellular Layout Hexagonal grid, 19 sites, 3 sectors per site Number of average dropped users per cell 10 Simulated Link Downlink Deployment scenario Urban Macrocellular (UMa) Traffic model Full buffer Packet Scheduling TMS (Transmission Mode Switching), Round Xxxxx. Adaptive Modulation and Coding 3GPP LTE standard transport formats; AMC PERtarget = 10% Xxxxxxxxx 00 XXx (00 XXX) Channel model Spatial Channel Model (SCM) Interference Explicit (the 9 strongest interference cells are considered) Number of antenna elements (BS, UE) (2, 2) Antenna separation (BS, UE) (λ/2, λ/2) Link to system interface Mutual Information Effective SNR Mapping (MIESM) HARQ Stop and wait; synchronous adaptive Number of HARQ processes 8 Retransmission interval 8 ms Maximum numer of retransmissions 3 (corresponding to a maximum of 4 transmissions) CQI reporting Wideband CQI, no PMI on PUCCH (mode 1-0) UE channel Estimation Realistic (embedded into link level performance curves) Preliminary simulations have been performed in order to make a comparison between a SIMO 1x2 system and a MIMO 2x2 system in which transmission modes are statically chosen (Transmit Diversity or Spatial Multiplexing) or dynamically managed by means of the TMS scheduler described in the above. Results depicted in Figure 4-25 show the benefit obtained when using the TMS scheduler, thus improving the cell throughput distribution with respect to Spatial Multiplexing performances. Cell throughput CDF; LTE system - dynamic TMS scheduler performances BW = 10 MHz - SCM channel - 57 cells, wrap around - 10 average dropped users per cell SIMO 1x2 (Round Xxxxx scheduler) MIMO 2x2 - Transmit Diversity (Round Xxxxx scheduler) MIMO 2x2 - Open Loop SM (Round Xxxxx scheduler) MIMO 2x2 - TMS scheduler (TxD and OL-SM) 0.8 0.7 Probability

Simulation parameters. Simulation Parameter Value Number of base stations 3 19 sites, 57 cells Number of users 10 Users / cell Number of antennas at each base station 4 Number of antennas at each user 1 Antenna type at the base station 3GPP path loss and shadowing models Antenna spacing 0.5λ Uniform Linear Array Inter site distance (ISD) 500m Minimum distance between UE and cell 35m Number of channel realizations at each position 1000 Centre frequency 2 GHz Channel Model one OFDM subcarrier, uncorrelated Rayleigh fading, perfect link adaptation PF scheduler MMSE receiver Cell radius 500 m Figure 4-26 shows the performance of the proposed coordinated scheduling algorithm compared to the non-coordinated scheme. The proposed algorithm is also evaluated on the scheduling criteria. In fact, the algorithm is first considered with the constraint on the choice of the selected cells (CoMP1) and second when this constraint is lifted (CoMP2). The performance of the proposed algorithm when the chosen cell is selected regardless the number of its cell-edge users is shown by the black curve. When the selected cell is the one which has the maximum number of cell-edge users, the algorithm remains better than the no-CoMP but falls below the CoMP1 since the other cells are constrained to transmit with lower powers in order to maximize the worst SINR margin of the selected cell having greater number of cell-edge users. Moreover the power distributed for the cell-edge users of the selected cell comes from the users far from the cell-edge which explains the loss shown from the 0.7 (b/s/Hz) at the average user spectral efficiency. Figure 4-26: Performance of the iterative optimization algorithm. We have plotted in Figure 4-27 the Jain index obtained for the iterative algorithm CoMP1. We can see that the Jain index decreases slightly as the number of UEs increases. The values of the Jain index don’t exceed 0.8 which leads to consider the proposed algorithm as fair for the different users in the network.

Simulation parameters. Simulation Parameter Value Channel Model 3GPP case 1 (SCME, 3D antenna model) ISD (inter-site distance) 500 m Velocity 3 km/h eNB antenna 4 antennas, 0.5 λ spacing UE antenna 1 antenna Channel estimation ideal System bandwidth 10 MHz Duplex method FDD Traffic model Full buffer Number of cells 21 (7 sites with 3 cells each), wrap around Number of UEs per cell 15 (average) Some results are shown in Figure 4-31. The case with fixed downtilt of 12° and no coordination is the baseline. The performance of fixed downtilt and coordination with 3 constraints (dark green dashed curve) and the performance of two fixed downtilts without coordination (0 constraints) are almost identical and provide 6% gain in spectral efficiency or about 19% gain in cell edge throughput. If applying coordination to the case of two fixed downtilts an additional improvement according to the number of considered constraints (1, 2 or 3 constraints) is feasible. For 3 constraints (light green solid curve) an additional gain of 6.5% in spectral efficiency or 17% in cell edge throughput is achieved. A slightly higher performance is possible when using optimum downtlit to the UE (light green dashed curve). 1050,0 17% gain at cell edge 1000,0 (same spectral efficiency) ] 950,0 s / bit 900,0 k [ 850,0 put alpha = 3 alpha = 2 800,0 ough 750,0 Th 700,0 19% gain at cell edge r (same spectral efficiency) E alpha = 1 U 650,0 ile 600,0 6.5% gain in spectral efficien - 5% 550,0 (same cell edge throughput) 0 - fixed DT 12° 6% gain in spectral efficiency 500,0 3 - fixed DT 12° 3 - opt. XX xxx. to 11° (same cell edge throughput) 450,0 0 - two DT: 19°/12° 400,0 1 - two DT: 19°/12° 2 - two DT: 19°/12° 350,0 3 - two DT: 19°/12° alpha = 0.5 300,0 1,5 1,6 1,7 1,8 1,9 2,0 2,1 2,2 2,3 2,4 2,5 2,6 2,7 spectral efficiency [bits/s/Hz] 1100,0 1050,0 1000,0 950,0 900,0 17% gain at cell edge (same spectral efficiency) 850,0 800,0 750,0 700,0 650,0 600,0 550,0 500,0 450,0 400,0 350,0 300,0 alpha = 3 alpha = 2 19% gain at cell edge (same spectral efficiency) alpha = 1 6.5% gain in spectral efficiency (same cell edge throughput) 0 - fixed DT 12° 3 - fixed DT 12° 3 - opt. XX xxx. to 11° 0 - two DT: 19°/12° 1 - two DT: 19°/12° 2 - two DT: 19°/12° 3 - two DT: 19°/12° 6% gain in spectral efficiency (same cell edge throughput) alpha = 0.5 1,5 1,6 1,7 1,8 1,9 2,0 2,1 2,2 2,3 2,4 2,5 2,6 2,7 spectral efficiency [bits/s/Hz] 5%-ile UE Throughput [kbit/s] Figure 4-31: Performance of 3D beam coordination versus number of constraint...

Simulation parameters. Mobile WiMAX network capacity [Mbps] 60 Non-IPTV traffic arrival Poisson IPTV call arrival Poisson Requested BW (maximum required bandwidth) by a IPTV channel [Mbps] 2 Maximum number of IPTV channels 30 Minimum required bandwidth to broadcast a IPTV channel [Mbps] 1 Maximum allowable bandwidth for the IPTV services [Mbps] 40 t1 [minute] 1 T [minute] 60 Let, be the time period for which every interval the required bandwidth for the IPTV services is observed. Then, the required dynamically reserved bandwidth BR(t) for the IPTV calls in proposed scheme can be calculated as:  N    B IPTV (t  nt1 )  B (t)  min  n1 ,  