Building up a simulation environment which fit in with real situations simulates the operations of the systems correctly. In section 6-1, the simulation environment setting and simulation techniques are introduced. Simulation results and discussions are presented in section 6-2.
6-1 Simulation Environment and System Parameters
In the wireless communication environment, two types of fading effects that characterize mobile communication: large-scale fading and small-scale fading. Large-scale fading represents the average signal power attenuation or the path loss due to motion over large areas.
This phenomenon is affected by prominent terrain contours (e.g., hills, forests, clumps of buildings, etc.) between the transmitter and receiver. The receiver is often said to be shadowed by such prominences. The statistics of large-scale fading provide a way of computing an estimate of path loss as a function of distance between the transmitter and receiver.
Large-scale fading is often described in terms of path loss and shadow fading. On the other hand, small-scale fading characterizes the rapid fluctuations of the received signal strength over very short travel distances or short time durations. These three kinds of fading models are described in the following sections.
Path Loss Model
Path loss describes the mean signal strength attenuation versus distance. In this thesis, we adopt the WINNER C2 model proposed by IST-WINNER project [25]. This model is obtained from the measurement in the urban environment. The mathematical description of the model is as follows:
( )
35 log10( ) 38.4 20 *log10 5 : Carrier Frequency (GHz): Distance between the mobile and the BS (meter).
carrier
carrier
PathLoss d d f
f d
⎛ ⎞
= ⋅ + + ⎜ ⎟
⎝ ⎠
(23)
Shadow Fading Model
When there are obstacles in the propagation path between the BS and the mobile, the received signal quality is fluctuated. Owing to user’s movement, signal propagation path will be changed, and thus, the receiver is not shadowed by such obstacles anymore. Shadow fading describes this kind of phenomenon which is the variations about the mean signal strength.
According to the measurements from real wireless propagation surroundings, the shadow fading is log-normal distributed. Therefore, if we do not consider special terrain, we would model the shadow effect as a log-normal random variable to generate the shadow fading effects in the simulations. The standard deviation of this random variable depends on the simulation surroundings. According to the measurement results proposed by WINNER C2 model, the standard deviation is 8 dB [25].
The most popular autocorrelation model of the shadow fading is the model proposed by Gudmundson [26]. Based on the measurement results, Gudmundson proposed the decreasing correlation function as follows:
2 | |
( ) : the correlation of two points.
: the standard deviation of shadow fading.
: the samples between two points.
: the correlation factor which represents the autocorrelation coefficient between
: the mobile velocity.
: the sampling period.
: the de-correlation distance obtained by measuring the environments : the correlation between two points separated by distance D.
D
v T D ε
If the correlation factor is equal to 0.5, the decreasing correlation function proposed by Gudmundson can be expressed as:
| |
( )
ln 2x
x e
Dρ Δ =
−Δ (25)Where
( )
: autocorrelation coefficient between two samples : the distance between two samples which is equal to : the de-correlation distancex
x k T v
D ρ
ΔΔ ⋅ ⋅
Small-scale fading
In a cellular mobile radio environment, the surrounding objects, such as houses, building or trees, act as reflectors of radio waves. These obstacles produce reflected waves with attenuated amplitudes and phases. If a modulated signal is transmitted, multiple reflected waves of the transmitted signal will arrive at the receiving antenna from different directions with different propagation delays. These reflected waves are called multi-path waves. Due to the different arrival angle and times, the multi-path waves at the receiver site have different phases. These reflected waves are combined together constructively or destructively giving rise to received signal fading at the receiver site depending on the phases of the reflected waves. This is called multi-path channel effect.
To simulate the effects of multi-path channels, the most important thing is to simulate the scattering effects. One of the effective channel simulators has been suggested by Jake. Jake’s model [15,28] assumes that reflector close to the mobile are finite and uniformly distributed over two-dimension plane. As the mobile moves toward a specific direction, the Doppler frequency offset of each reflected signals can be obtained. Because the antenna of the cell phone is usually omni-directional, the received signal is the sum of all reflected signals. The scattering environment considered by Jake is shown in Fig. 35.
Fig. 35 The angle of incidence considered by Jake’s model
The mathematical description of the Jake’s model is
( )
( )r t
is the received signal, which can be represented as( )
In the simulation, we choose voice over IP (VoIP) as the real-time application. The VoIP model G.728 proposed by International Telecommunication Union (ITU) is adopted in the simulation. G.728 codec is low delay speech coder standard, for compressing toll quality
speech (8000 samples/second). G.728 coders are widely used for applications that require very low algorithmic delay. The typical application of this speech coder is in telephony over packet networks. G. 728 codec are based on the principle of Low Delay-Code Excited Linear Prediction (LD-CELP).
The VoIP packet arrivals every 20ms, which is equal the frame length. For G. 728 Codec, the data rate is 16 kbits/sec, and therefore, voice IP data in a packet is 320 bits. Besides, the RTP header requires 12 bytes, UDP header requires 8 bytes, and IP header requires 20 bytes, so each VoIP packet contains 640 bits in our simulation.
Wrap Around Technique
Co-channel interference is one of the important factors in the wireless communication systems, and must be considered in the simulation. According to the analyses proposed by Lee and Miller [28], if two-tier interference sources are considered, the co-channel interference can be simulated approximately equal to infinite interference sources. Therefore, only two-tier interference sources are simulated in our simulation. At the borders of the defined coverage area cell wrapping is applied in order to provide a realistic inter-cell interference and the possibility of handover in the border cells.
Fig. 36 Simulation environments with 19 cells
As shown in Fig. 36, if frequency reuse factor equal to one is applied, only the center BS, BS
#0, suffers two-tier interferences. All the other BSs cannot suffer entire two-tier interferences.
For this reason, only the center BS has useful statistical information, and this kind of simulation is non-efficient. We adopt wrap around technique which lets all BSs suffer entire two-tier interferences. Wrap around means when the user leave from one edge, the user will
enter the cell on the opposite edge. As shown in Fig. 37, if the user leaves from cell #13, he/she will enter the cell #17 to avoid boundary effect such that handoff can be achieved.
Besides, if the serving cell is BS #7, the two-tier interfering BSs are mark by the green color.
Therefore, wrap around technique can not only avoid boundary effect but also generate two-tier interferences.
0
Fig. 37 Simulation environments when the wrap around technique is applied
Link Budget
The link budget consider that the total bandwidth is 6 MHz, the environment temperature is 293K, the frequency reuse factor is 4, FFT size is 2048, carrier frequency is 2.5 GHz, and PUSC with 3 sectors is used.
Modulation Scheme QPSK
Coding Rate 1/2
Transmitter(BS)
Max. Transmit Power for each BS [dBm] 46.0206 (40W) Max. Transmit Power for each Sector [dBm] 41.2494 (13.3333W) Max. Transmit Power for each Subchannel [dBm] 28.2391 (0.6667W) a
BS Antenna Gain [dBi] 18 b
Back Off [dB] 5 c
EIRP Per Sub-channel [dBm] 41.2391 d=a+b-c
Receiver (MS)
Thermal Noise Density [dBm/Hz]=KT -173.9325 e
Noise Figure [dB] 6 f
Receiver Noise Density [dBm/Hz] -167.9325 g=e+f
Receiver Noise Power[dBm] -118.8828 h
Received Interference Power [dBm] -113.1265 i
Total Received Noise and Interference Power [dBm] -112.1032
Required Eb/ (No+Io) [dB] 6 j
Mobile Antenna Gain [dBi] 0 l
Required Received Signal Power[dBm] -106.1032 m
Max. Allowable Propagation Loss [dB] 147.3423 n=d-m
Coverage Probability [%] 90
Log Normal Fading Constant [dB] 8 o
Log Normal Fading Margin [dB] 10 p
Allowed Path Loss for Cell Range [dB] 137.3423 r=n-p
Corresponding Cell Radius [m] 1000
Table 2 Link budget for PUSC
6-2 Simulation Results
Simulation results are presented in this section. Both distributed and adjacent sub-carrier sub-channelization methods are taken into consideration. Different moving speed of users is also considered in the simulation. We first analyze that different threshold setting influences the performances and resource consumption without using the FFR cell structure. After that, the performances under the FFR cell structure are presented.
We simulated the average packet loss rate, ping-pong rate, and average diversity set size for FBSS and MDHO with and without fractional frequency reuse. In the average packet loss rate calculation, we assume that the packet is loss if any bit within this packet is not received correctly by the mobile. Average diversity set size can be treated as the network layer resource consumption because the data need to be forwarded to all the BSs in the diversity set through backhaul network. The ping-pong effect causes the MS to switch the serving BS between the new and old BSs back and forth, and therefore, the MS need to perform handoff ranging with the new BS. Moreover, the radio link resource is wasted if unnecessary ping-pong effect occurs. The average ping-pong rate is defined as
ping pong
number of link layer handoff events with ping pong effect
R
− =total number of link layer handoff event
(28)Fig. 38 shows the average packet loss rate during handoff without using the FFR cell structure under the PUSC mode. Th_add is the threshold to decide the initiation of network layer handoff. The time required for the network layer handoff and the variation speed of link layer signal quality should be considered altogether into the threshold setting. The higher value of Th_add, the more easily Best Candidate Set BS can be added into the diversity in which the data path of the network layer will be established. Under this circumstance, it will be much easier to finish the data path before Th_change condition is met but relatively consume network resources which is shown in Fig. 39. Besides, Th_change is the threshold to change serving BS. If the value of Th_change is too low, it would influence the decision of the link layer handoff and make the users switched between the new and the old BSs back and forth which cause the ping-pong effect to waste resources. If the value is too high, it helps increase
the accuracy of deciding the handoff but decrease the users’ link and service quality. So, Fig.
38 shows that as the Th_change increases, the average packet loss rate decreases. But low Th_change might cause high ping-pong effect which is shown in Fig. 40.
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's speed : 50km/hr, PUSC)
Average Packet Loss Rate
Fig. 38 Average packet loss rate vs. Th_add (50km/hr, PUSC)
0 2 4 6 8 10 12 14 16 18
Average Diversity Set Size vs. Th_add
Fig. 39 Average diversity set size vs. Th_add
2 4 6 8 10 0.34
0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50
Average Ping-pong rate
Th_change (dB)
Average Ping-pong Rate vs. Th_change
Fig. 40 Average ping-pong rate vs. Th_change
Fig. 41, 40, and 41 show the average packet loss rate under the FFR cell structure when the users’ moving speed is 50km/hr and the PUSC is applied. If the Th_use is set to be smaller, the average packet loss rate decreases because the handoff user is more easily served by the arranged handoff sub-channels. On the other hand, the smaller value of Th_use will lead to larger radio resource consumption which is shown in Table 3. Table 3 shows average portion of time that handoff sub-channel is used. In this table, we can observe that if Th_use is set to be larger, the smaller portion of time needs to be allocated for handoff sub-channel. Besides, as the Th_use is fixed, if the Th_change value is set to be larger, the user is harder to change the serving BS to the BS with better channel quality. This case causes the handoff sub-channel usage rate increase.
According to what is mentioned above, the value of Th_change can be set higher to make the correct decision for the handoff and to reduce the occurrence of the ping-pong effect if the user is served with the sub-channels. If the Th_change condition is met but the network data path is not completely established yet, on the other hand, the problem of the poor signal quality with the serving BS which could also lower service quality can be solved by using the
sub-channels.
Average Packet Loss Rate vs. Th_add (User's Speed: 50km/hr, PUSC)
Fig. 41 Average Packet Loss Rate vs. Th_add with Th_use=1 (50km/hr, PUSC)
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 50km/hr, PUSC)
Average Packet Loss Rate
Fig. 42 Average Packet Loss Rate vs. Th_add with Th_use=3 (50km/hr, PUSC)
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 50km/hr, PUSC)
Average Packet Loss Rate
Fig. 43 Average Packet Loss Rate vs. Th_add with Th_use=5 (50km/hr, PUSC)
2 4 6 8 10
1 0.085 0.099 0.116 0.133 0.15
3 0.044 0.055 0.070 0.086 0.1
5 0.023 0.027 0.036 0.050 0.065
Table 3 Portion of time that RF=7 sub-channels is used (User’s speed: 50km/hr)
According to the mentions above, we can use the network and radio resource consumption to determine which combination of thresholds should be used. For example, if the average diversity set size has to be smaller than 1.8, the usable Th_add range is 0~12. Moreover, if the portion of time allocated to handoff sub-channels has to be smaller than 0.1, the possible combinations are Th_use=3~5 with Th_change=0~10 and Th_use=1 with Th_change=2~4.
Besides, if the ping-pong rate is constraint to be smaller than 0.4, the Th_change should be set to be larger than 4. Using the resource constraint and performance requirement, the optimal threshold setting can be obtained.
Fig. 44 43, 44 are the average packet loss rate performance with different Th_use values under the FFR cell structure when the moving speed of users is 120km/hr and PUSC is
Th_change Th_use
applied. Table 4 shows the portion of time need to be allocated to handoff sub-channels. The same phenomenon can be obtained. The difference is that when the user velocity is 120km/hr, the average packer loss rate under FBSS with FFR is better than that under MDHO mode.
Because the MDHO can improve the signal quality only when the network layer data path establishment which requires time to process is completed. As the user’s moving speed increases, the connection quality also change rapidly. Therefore, the FBSS with FFR can rescue the user whose connection quality is bad without any processing time while MDHO can rescue the user only when the data path establishment is completed. When the user’s velocity is not very fast, MDHO has beer average packet loss rate performances because the user’s signal quality does not change rapidly, and the data path establishment can be completed before the connection quality of the user becomes poor.
2 4 6 8 10
1 0.179 0.187 0.197 0.208 0.219
3 0.126 0.132 0.142 0.153 0.162
5 0.086 0.090 0.096 0.106 0.116
Table 4 Portion of time that RF=7 sub-channels is used (User’s speed: 120km/hr)
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 50km/hr, PUSC)
Average Packet Loss Rate
Fig. 44 Average Packet Loss Rate vs. Th_add with Th_use=1 (120km/hr, PUSC)
Th_change Th_use
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 50km/hr, PUSC)
Average Packet Loss Rate
Fig. 45 Average Packet Loss Rate vs. Th_add with Th_use=3 (120km/hr, PUSC)
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 50km/hr, PUSC)
Fig. 46 Average Packet Loss Rate vs. Th_add with Th_use=5 (120km/hr, PUSC)
Fig. 47~50 show the average packet loss rate when the band AMC sub-carrier sub-channelization is performed. Impact on average packet loss rate for different threshold setting and user’s velocity is also shown in these figures. In the simulation, we assume that
the serving BS will schedule the handoff user’s using the sub-channels with the best channel condition from the user’s perspective. The user need to report the channel gains of each sub-channel to the serving BS, and the reporting delay is 20 milliseconds which is equal to the frame length.
Average Packet Loss Rate vs. Th_add (User's Speed: 50km/hr, BandAMC)
Average Packet Loss Rate
Fig. 47 Average Packet Loss Rate vs. Th_add with Th_use=1 (50km/hr, BandAMC)
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 50km/hr, BandAMC)
Average Packet Loss Rate
Fig. 48 Average Packet Loss Rate vs. Th_add with Th_use=3 (50km/hr, BandAMC)
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 50km/hr, BandAMC)
Average Packet Loss Rate
Fig. 49 Average Packet Loss Rate vs. Th_add with Th_use=5 (50km/hr, BandAMC)
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 120km/hr, BandAMC)
Average Packet Loss Rate
Fig. 50 Average Packet Loss Rate vs. Th_add with Th_use=1 (120km/hr, BandAMC)
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 120km/hr, BandAMC)
Average Packet Loss Rate
Fig. 51 Average Packet Loss Rate vs. Th_add with Th_use=3 (120km/hr, BandAMC)
0 2 4 6 8 10 12 14 16 18
Average Packet Loss Rate vs. Th_add (User's Speed: 120km/hr, BandAMC)
Average Packet Loss Rate
Fig. 52 Average Packet Loss Rate vs. Th_add with Th_use=5 (120km/hr, BandAMC)
Conclusions
In this thesis, at first, we introduce the system architectures of IEEE 802.16e-2005, including frame structure, sub-channelization process, and handoff mechanisms. We also introduce the cell planning and frame structure design for fractional frequency reuse cell structure. Based on the IEEE 802.16e-2005, we modified FBSS initiation algorithm such that it can take advantages on FFR cell structure. The signaling procedures for the modified FBSS algorithm are also presented in this thesis.
The proposed fast handover ranging requires only two frames to complete link layer handoff, which can meet the real-time services requirements. Besides, network layer handoff effect on the link quality can be easily alleviated by using the arranged handoff sub-channels at the right moment. Furthermore, the value of Th_change can be set higher to make the correct decision for the handoff and to reduce the occurrence of the ping-pong effect if the user is served with the arranged handoff sub-channels. If the Th_change condition is met but the network data path is not completely established yet, on the other hand, the problem of the poor signal quality with the serving BS which could also lower service quality can be solved by using the arranged handoff sub-channels.
Simulation results show that handoff performance can be improved if the serving BS serves its handoff users using the arranged handoff sub-channels. Without any resource constraint, the average packer loss rate can be improved up to 60%. If the user’s moving speed is very high, ex. 120 km/hr, the average packet loss rate of FBSS with FFR is better than that of MDHO.
References
[1] P. Nicopolitidis, et al., Wireless Networks, John Wiley & Sons, New Jersey, 2003.
[2] Y.B. Lin and A.C. Pang, Wireless and Mobile All-IP Networks, Wiley, Indiana, 2005.
[3] J.C. Chen and T. Zhang, IP-Based Next Generation Wireless Networks, John Wiley &
Sons, New Jersey, 2004.
[4] J. Zander and S.L. Kim, Radio Resource Management for Wireless Networks, Artech House, 2001.
[5] WiMAX Forum, “Mobile WiMAX --- Part I: A Technical Overview and Performance Evaluation,” WiMAX Forum, June 2006.
[6] WiMAX Forum, “Mobile WiMAX --- Part II: A Comparative Analysis,” WiMAX Forum, May 2006.
[7] Qualcomm, “Description and simulations of interference management for OFDMA based E-UTRA downlink evaluation,” 3GPP TSG-RAN WG1 #42, R1-050896, 2005.
[8] E. Shim, et al., “Low Latency Handoff for Wireless IP QoS with NeighborCasting,” IEEE
ICC, vol. 5, pp.3245-3249, May 2002.
[9] Y. Gown, et al., “Fast Handoff in Wireless LAN Networks Using Mobile Initiated Tunneling Handoff Protocol for IPv4 (MITHv4),” IEEE WCNC, vol. 2, pp.1248-1253, March 2003.
[10] I.F. Akyidiz, et al., “A Survey of Mobility Management in Next-Generation All-IP-Based Wireless Systems,” IEEE Wireless Comm., vol. 11, pp16-28, Aug. 2004.
[11] F. Feng and D.S. Reeves, “Explicit Proactive Handoff with Motion Prediction,” IEEE
WCNC, vol. 2, pp.855-860, March 2004.
[12] IEEE Standard 802.16e, “Part16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems,” Feb. 2006.
[13] J.G. Proakis, Digital Communications, 4th Edition, McGraw-Hill, New York, 2000.
[14] T.S. Rappaport, Wireless Communications, Second Edition, Prentice Hall, New Jersey, 2002.
[15] G.L. Stuber, Principles of Mobile Communication, Kluwer Academic Publishers, 1996.
[16] R. Prasad, OFDM for Wireless Communications Systems, Artech House, 2004.
[17] S.G. Glisic, Advanced Wireless Communications, John Wiley & Sons, 2004.
[18] Ericsson, “WiMAX – Copper in the Air,” Ericsson White Paper, April 2006.
[19] WiMAX Forum, “WiMAX End-to-End Network Systems Architecture,” WiMAX Forum, Feb. 2006.
[20] S. Das, et al., “System Aspects and Handover Management for IEEE 802.16e,” Bell Lab.
Technical Journal, pp.123-142, 2006.
[21] S. A. Kyriazalos and G. T. Karetsos, Practical Radio Resource Management in Wireless
Systems, Artech House, Norwood, MA, 2004.
[22] Y. Chen, “Soft Handover Issues in Radio Resource Management for 3G WCDMA Networks,” Dissertation of doctor philosophy, Queen Mary University, London, Sept.
2003.
[23] Wen Tong, et al., “OFDMA PHY Layer Support for SHO Based Macro-Diversity Transmission,” IEEE C802.16e-04/165r1, July 2004.
Available: http://www.ieee802.org/16/tge/contrib/C80216e-04_165r1.pdf
[24] T.T. Kwon, et al., “Mobility Management for VoIP Services: Mobile IP vs. SIP,” IEEE
Wireless Comm., vol. 9, pp. 66-75, Oct. 2002.
[25] D.S. Baum, et al., “Final Report on Like Level and System Level Channel Models,”
IST-WINNER, Nov. 2005.
[26] M. Gudmundson, “Correlation Model for Shadow Fading in Mobile Radio Systems,”
Electronics Letters, vol. 27, pp.2145-2146, Nov. 1991.
[27] W.C. Jake, Microwave Mobile Communications, Wiley, New York, 1974.
[28] J.S. Lee and L. E. Miller, CDMA Systems Engineering Handbook, Artech House, 1998.