Simulation results. 4.1. Vehicle parameters and the values used in the simulation that are not taken from the actual test vehicle (implicit):
Simulation results. Esitmated value g(1000,0.01,w ) Simulated value L 400 350 The number of bits leaked to Eve 300 250 200 150 100 50 0 50 100 150 200 250 300 350 400 450 500 Block length w in pass 1
Simulation results. This section presents some numerical examples illustrating the performances of our proposed schemes and finally compared together. For simplicity, the scenario is as- sumed with a single secondary BS serving two secondary users and a single primary cell-edge user within the cognitive cell. It is also assumed that there is one primary user per primary cell which is located in the outer part of the cognitive cell, but within the close vicinity. Note that each user is equipped with a single antenna. As shown in Fig. 3.1, secondary and primary cell-edge users within the cognitive cell are located in sector 3, i.e. q=3. The experiment is done with a single scatterer, i.e, Q = 1. The angular spread of local scatters surrounding the users is to be assumed 2 degrees. The spacing distance between the array elements is λ/2. The carrier frequency is 2 GHz. The noise variance plus the intercell interference is set to 1. In this simulation, SeDuMi solver under optimisation solver CVX [6], [89] is used to attain the optimal solution for the problems stated in (3.16) and (3.21). The azimuth directions (angle of propagation with respect to the antenna array broadside) of the users as well as the angular spread due to the local scatters cor- responding to the sector of the secondary BS can be estimated using the algorithm
Simulation results. Initialization: To initialize, Ƈ chooses a random bit from the given set {0, 1} and constructs a secure hash-digest function to respond to the queries from Ɲ, where h0=hqi be the pseudorandom function, while h1 is a random function. and ∏ Training: The Ɲ chooses the nonce Rg and NIDi in the protocol, F1: Anonymity, F2: Mutual Authentication, F3: Resist Man in the middle Attack, F4: Resist unjustified failures of login attempts, F5: Supports forward secrecy, F6: Resist impersonation attack, F7: Supports Session key security, F8: Resist Denial of service attack, F9: Biometric security (3-factor authentication) F1 × × ✓ ✓ × ✓ ✓ ✓ F2 × × ✓ × ✓ ✓ ✓ ✓ F3 × × × ✓ ✓ ✓ ✓ ✓ F4 ✓ ✓ ✓ ✓ ✓ × ✓ ✓ F5 × × × ✓ × ✓ ✓ ✓ F6 × × ✓ × × ✓ ✓ ✓ F7 × × ✓ ✓ × × ✓ ✓ F8 × × × × × ✓ × ✓ F9 × × × × × ✓ × ✓ Similarly, the event beginESP (bitstring) and event endESP(bitstring) are employed by ESP to authenticate Ui. We compute the results of queries and the order of the two Ui,S ,∏ and models ∏ S,Ui by responding the queries, pair of events remained stable. The results in Fig. 3 depict that GN,S Execute(∏ S,GN ) and Send(∏ ,), respectively. our scheme achieves mutual authentication and session key Ui,S Ui,S Test (∏ ): Using this query, if qh is constructed, Ɲ selects at secrecy since the session key is robust against attackers. random v ∈{0, 1}, then it responds by returning legal session key qh in case v = 0, or any random string if v =1. Or else, Ɲ returns φ, indicating null string or emptiness. R,T Test(∏ ): Its modeling is also similar to above query. Challenge: The Ȃ submits the Test query toward Ɲ after VI. PERFORMANCE ANALYSIS In this section, we examine the performance of proposed scheme with other contemporary authentication schemes for smart grids. Table I depicts the comparative analysis of GN,S having queried the oracle Execute (∏ ,∏ ). S,GN features and performance efficiency between our scheme and other protocols, which manifests that the schemes [5-8, 10-12] Guess: Upon having queried (∏ ) or (∏ ), the Ȃ outputs a bit Ui,S S,Ui are unable to ensure the requisite security properties of an b as 0, if it takes the responded message as valid session key, or else it outputs b as 1. Finally, Ɲ produces the b' as 0 if b'=b, or else it will return the output as b'=1. The probability analysis for b'=b is alike the analysis performed in Lemma 1. The Ȃ could win this game if it guesses the equality for b'=b having the real experiment-based probability as ( + 1/2), i.e. b=0. The Ȃ ...
Simulation results. Box and whisker plot (median, first and third quartiles, range) of estimates of the survival function minus the true quantity. Comparison of estimates when using inverse Gaussian (IG), Xxxxxxx (W), Xxxxxx Xxxxx (KM) and growth curve (GC) with a threshold models at the median failure time for each experimental factor detailed below plot.
Simulation results. Under WiBro/WiMAX MIMO downlink environment, computer simulation is progressed on many MIMO schemes. There are simulation parameters on Table. 1. An assumption is that channel information is perfectly known at receive antenna. In case of 2×1 and 2×2 STTD, figure 5 is simulation result. As shown in figure 5, if the system has more transmit antennas and receive antennas, it obtains a diversity gain like MRC of diversity system. Figure 6 is a simulation result of 2×2 SM for many SM schemes and figure 7 is a throughput curve of ML, ZF, MMSE, SIC, OSIC. As shown in figure 6, ML detection is the most optimal and based on zero forcing, SIC and OSIC seem to be better performance than ZF detection. But MMSE has a better performance than SIC and OSIC. In figure 7, when applying many SM schemes, their 1x1 2x1 STC 2x2 STC 1x1 ML ZF MMSE SIC OSIC 1 400 350 0.1 300 Throughput(kbps) 250 BER
Simulation results. In all the simulations, the power of the additive white Gaussian noise is set to 0 dB. Then, for each distance d a simulation is run with transmit power(eNodeB1) = SNR(d) and transmit power(eNodeB2) = SNR(ISD-d), for each scenarios. The performance curves are given in terms of BER and throughput, for the 3 modes of Table 1. The throughput is given in percentage of the maximum achievable throughput, i.e. 100x(1-FER). The Inter Site Distance is 500 m.
Simulation results. This section presents a performance analysis of the proposed beamforming system. Performance analysis of the smart antenna system is shown in terms of BER(Bit Error Rate) in WiBro/WiMAX Uplink environment. Simulation parameters are listed in Table 1. Figure 3 shows BER performance of smart antenna system as the number of antenna elements increases. As shown in figure 3, it is observed that the BER performance do not becomes much better as the number of antenna elements increases. Figures 4 and 5 illustrate the BER performance of single antenna system, beamforming technique of using one eigenvector(e1) and beamforming technique of using two eigenvectors(e1, e2) in multi-path fading channel. Figures 4 and 5 show the performance of the beamforming system in vehicular and pedestrian channel environment, respectively. Table 1. Simulation parameters Parameters Value Subcarrier Permutation Mode PUSC Carrier Frequency 2.3 GHz Channel Bandwidth 8.75 MHz FFT Size 1024 point Number of Data Subcarriers 720 Number of Pilot Subcarriers 120 TDD Frame Length 5ms CP(Cyclic Prefix) Ratio 1/8 Doppler Frequency 127 Hz Data Modulation QPSK Channel Coding (Coding Rate) Convolutional Coding (1/2) Number of Tx Antennas 1 Number of Rx Antennas 1 or 4 Number of Used Slots 20
Simulation results. We compare the performance of our share-assigning scheme with the simple static scheme of allowing all ingress nodes to send at the SLA rate for different scenarios. This would allow all valid configurations of traffic mix but at the same time would introduce a lot more traffic into the network than that allowed by the SLA. In the first scenario we developed a simple star core topology as shown in figure 4a. We use three source domains S1, S2 and S3 and one destination domain. In each domain there are 30 ftp traffic sources, each one attached to a separate node. The delays inside the domains are 10usec while the delays and bandwidths in the core are shown in the figure. Thus the round-trip delays for the domains will depend on the delays in the core network. All the sources S1 30Mb 30Mb
Simulation results. The simulation considers a C-RAN consists of N = 3 neighbouring RRHs, each equipped with M = 8 antennas, transmitting to Ki = 6 single antenna ITs that are randomly generated in the network. The renewable energy generated from environmental sources at each local RRH is E1 = 3.5W, E2 = 0.2W and E3 = 0.5W, respectively. Lets further assume that the network operator has purchased B[ahead] = B[ahead] = B[ahead] = 1.5W amount of energy from the day-ahead mar-
