The system level performance of the SFBC macro-diversity combining with SM is inves-tigated in this section. The parameter used in the simulation are listed in Table 4.1 [25].
When the mobile station moves from the serving base station (BS 0) to the adjacent base station (BS 1) as shown in Fig. 3.1, we calculate the throughputs under different spatial correlation at the transmitter of each base station and compare with the single base station using SM or SFBC. The separation of each base station is far enough to ignore the spatial correlation between the transmit antennas of BS 0 and BS 1.
This simulation is investigated to show the throughput gain by adopting SFBC macro-diversity combining with SM around cell edge compared with that of the conven-tional centralized MIMO-OFDM scheme and then reform the performance of MDHO. Per-fect channel state information at receivers is assumed during the simulations.
Table 4.1: Simulation Parameters
Parameter Value Remark
Center frequency 2.5 GHz Channel bandwidth 10 MHz
FFT size 1024
Cyclic prefix ratio 1/8
Guard sub-carriers (Ng) 159 Right : 80 , Left : 79 Used sub-carriers (Nu) 865
Site-to-site distance 1.5 km Transmit power 37 dBm (5W)
Path loss 130.62+37.6log(d) d:km Thermal noise density -174 dBm
Channel model ITU Pedestrian B
From Fig. 4.3, with spatial correlation at the transmitter ρtx = 0.1, it is observed that mobile station can get huge throughput close to the serving base station using SM, but the throughput decreases seriously with the distance from the serving base station.
The throughput performance of SM reaches 1 bit/s/Hz around cell edge which is similar
0 250 500 750 1000 1250 1500 0
1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughputs (bit/s/Hz)
SM 2x2 corr=0.1 SFBC 2x2 corr=0.1 DSFBC−SM 4x2 corr=0.1
1 bit/s/Hz
1 bit/s/Hz
Figure 4.3: Throughputs of SM 2x2 , SFBC 2x2 and DSFBC-SM 4x2 with ρtx = 0.1 at each BS.
to SFBC. The poor throughput around the cell edge is improved obviously by applying distributed SFBC technique at the base station sides. Because of the equal receive signal strength from each base station, using transmit diversity at the base station sides increases SINR quite effectively around the cell edge, thereby allowing for choosing higher order MCS in Table II for higher spectrum efficiency. Therefore, the SFBC macro-diversity combining with SM scheme can overcome the drawbacks of the single base station using SM and maintain the large throughput for mobile station around cell edge. Even if the offered throughput satisfies the requirement of a mobile station, call-dropped frequency
27
can be reduced at the cell boundary and the ping-pong effect [12] can be relieved. Here the ping-pong effect means that the mobile station handovers back and forth several times between the adjacent base stations. Hence, the handover performance in OFDM cellular network is also enhanced greatly.
0 250 500 750 1000 1250 1500
0 1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughputs (bit/s/Hz)
SM 2x2 corr=0.3 SFBC 2x2 corr=0.3 DSFBC−SM 4x2 corr=0.3
1 bit/s/Hz
1 bit/s/Hz
Figure 4.4: Throughputs of SM 2x2 , SFBC 2x2 and DSFBC-SM 4x2 with ρtx = 0.3 at each BS.
The effects of increasing the spatial correlation of each transmitters at each base station are showed in Figs. 4.4, 4.5, 4.6 and 4.7. In a rich spatially-correlated channel, the throughput of the single base station using SM is worse as [11] [33] [34] and is even lower than that using SFBC around the cell edge. The SFBC macro-diversity combining with SM
0 250 500 750 1000 1250 1500 0
1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughputs (bit/s/Hz)
SM 2x2 corr=0.5 SFBC 2x2 corr=0.5 DSFBC−SM 4x2 corr=0.5
1 bit/s/Hz
1 bit/s/Hz
Figure 4.5: Throughputs of SM 2x2 , SFBC 2x2 and DSFBC-SM 4x2 with ρtx = 0.5 at each BS.
scheme can also provide higher throughput than the conventional SFBC and SM schemes withρtx= 0.7. Forρtx = 0.9, the throughput of the SFBC macro-diversity combining with SM scheme is the same as that of the conventional SFBC scheme. It is evident that the performance of SFBC macro-diversity combining with SM scheme is more robust than the conventional SM scheme as spatial correlation increases.
When the distance between a mobile station and the serving base station is two thirds of the cell radius, Fig. 4.8 shows that SFBC macro-diversity combining with SM scheme can provide higher throughput than the conventional SM and SFBC schemes for
29
0 250 500 750 1000 1250 1500 0
1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughputs (bit/s/Hz)
SM 2x2 corr=0.7 SFBC 2x2 corr=0.7 DSFBC−SM 4x2 corr=0.7
1 bit/s/Hz
1 bit/s/Hz
Figure 4.6: Throughputs of SM 2x2 , SFBC 2x2 and DSFBC-SM 4x2 with ρtx = 0.7 at each BS.
ρtx ≤ 0.7. For ρtx = 0.9, the throughput of the conventional SFBC scheme is higher than SFBC macro-diversity combining with SM scheme and the conventional SM scheme.
At the two thirds of the cell radius, the performance of SFBC macro-diversity combining with SM scheme is not effective due to the longer distance from the adjacent base station.
Because of path loss, the received signal strength from the adjacent base station is decreased seriously. When the distance between a mobile station and the serving base station is a cell radius. Fig. 4.9 shows that the throughput is quickly getting worse for the conventional SM and SFBC schemes. However, the performance of SFBC macro-diversity combining
0 250 500 750 1000 1250 1500 0
1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughputs (bit/s/Hz)
SM 2x2 corr=0.9 SFBC 2x2 corr=0.9 DSFBC−SM 4x2 corr=0.9
0.5 bit/s/Hz
Figure 4.7: Throughputs of SM 2x2 , SFBC 2x2 and DSFBC-SM 4x2 with ρtx = 0.9 at each BS.
with SM scheme at the cell boundary is better than that at the two thirds of the cell radius.
Because the distance from the serving base station is almost equal to the distance from the adjacent base station, the mobile station receives the equal signal strength from the two base stations. At the cell boundary, SFBC macro-diversity combining with SM scheme can offer higher throughputs than the conventional SM and SFBC schemes under different spatial correlations. Obviously the improvement of distributed SFBC is great,
In Table 4.2, we show increased throughputs of SFBC macro-diversity combining with SM scheme compared with the conventional SM scheme when a mobile station is at
31
0 0.1 0.3 0.5 0.7 0.9 1 0
0.5 1 1.5 2 2.5 3 3.5 4 4.5
Transmit antenna spatial correlation
Throughputs (bit/s/Hz)
SM 2x2 SFBC 2x2 DSFBC−SM 4x2
Figure 4.8: Throughputs of SM 2x2, SFBC 2x2 and DSFBC-SM 4x2 schemes for different ρtx with the distance from the serving BS equal to 500m (2Rc/3).
the two-thirds of the cell radius and a cell radius (Rc). It is shown that the improvements of throughput by SFBC macro-diversity combining with SM scheme is remarkable under spatially correlated channels. SFBC macro-diversity combining with SM scheme can offer higher data throughput for a mobile station at the cell boundary. Hence, the handover performance can be enhanced greatly.
0 0.1 0.3 0.5 0.7 0.9 1 0
0.5 1 1.5 2 2.5 3 3.5 4 4.5
Transmit antenna spatial correlation
Throughputs (bit/s/Hz)
SM 2x2 SFBC 2x2 DSFBC−SM 4x2
Figure 4.9: Throughputs of SM 2x2, SFBC 2x2 and DSFBC-SM 4x2 schemes for different ρtx with the distance from the serving BS equal to 750m (Rc).
Table 4.2: Throughputs Improvement by DSFBC-SM Compared with The Conventional SM.
Increased Throughputs
by DSFBC-SM (bit/s/Hz) ρtx= 0.1 ρtx= 0.3 ρtx= 0.5 ρtx= 0.7 ρtx= 0.9
Distance from the serving BS 500m (2Rc/3) 1 1 1 1 0
Distance from the serving BS 750m (Rc) 2 1 1 1 0.5
33
34
CHAPTER 5
Cyclic Delay Macro-Diversity Combining for MIMO OFDM Cellular Mobile Networks
In this chapter, we replace space-frequency block code (SFBC) with cyclic delay diversity (CDD) to increase transmit diversity at the adjacent suitable base stations. CDD can be viewed as a space-time block code. In contrast to SFBC, it is not necessary to assume constant channel properties over several sub-carriers or symbol and the number of transmit antennas. CDD is an efficient way to achieve diversity in a flat fading channel. Applying CDD only requires changes at the transmitter and the receiver remains changeless. The complexity of implementing CDD is very minimal. CDD inserts virtual echoes on the channel response. That increases the frequency selectivity of the channel seen by the re-ceiver [17] [22]. Thus, it reduces the likelihood of deep fading. CDD can achieve desirable transmit diversity gain over uncorrelated channel. Because the distance between the adja-cent is far enough for CDD free from spatial correlation, applying CDD at the adjunct base station sides is a more efficient way than at the transmit antenna sides of each base station.
Hence, we introduce the cyclic delay macro-diversity combining with spatial multiplexing scheme.
Figure 5.1: The Block Diagram of Cyclic Delay Macro-Diversity Combining with Spatial Multiplexing Scheme.
5.1 CDD Macro-Diversity Scheme
Fig. 5.1 shows the block diagram of two adjacent base stations apply CDD technique and each base station uses two antennas to perform spatial multiplexing. The signals are trans-mitted over the adjacent base stations, whereas it is only different with a specific cycle shift. After cyclic shifting, a cyclic prefix in add to avoid the ISI and maintain sub-carriers orthogonality for multi-path channels. δi denotes the cyclic shift of theith base station in the time domain and it can be regard as a phase factor as
θk = e−jNF F T2π δik. (5.1)
This phase factor linearly increase with the sub-carrier index k for each base station.
5.2 SINR Performance
Both the serving base station (BS 0) and the target base station (BS 1) use two transmit antennas to perform spatial multiplexing. Because we apply CDD with a cyclic shift at the
35
adjacent base stations side, the cyclic delay could be chosen according to [35].
δ0 = 0 , δi = N
Nbs + δi−1 . (5.2)
Figure 5.2: DCDD-SM Simplified System Model.
As depicted in Fig. 5.2,N is the number of used sub-carriers in the OFDM system and Nbs is the number of base stations in the scheme. We can get the cyclic shift δ1 = N/Nbsfor BS 1, and denote those modulated symbols assm. The sequences ofsmencoded by CDD can be separated into two groups for each base station as vectorsS0andS1 :
S0 = [s1 s2 s3 s4... s2N−3 s2N−2 s2N−1s2N] ; S1 =
s1e−j2πkδ1N s2e−j2πkδ1N ... s2N−1e−j2πkδ1N s2Ne−j2πkδ1N .
For the next SM branches,S0is split into two vectorsS10andS20. Also,S1 is split into two vectorsS11 andS21 for the transmit antennas of each base station. Thus, the output signals of the transmitters becomes
S10 = [s1 s3 s5 s7... s2N−3 s2N−1] S20 = [s2 s4 s6 s8... s2N−2 s2N] S11 =
s1e−j2πkδ1N s3e−j2πkδ1N s5e−j2πkδ1N s7e−j2πkδ1N ... s2N−3e−j2πkδ1N s2N−1e−j2πkδ1N
S21 =
s2e−j2πkδ1N s4e−j2πkδ1N s6e−j2πkδ1N s8e−j2πkδ1N ... s2N−2e−j2πkδ1N s2Ne−j2πkδ1N
.
wherek = 1, 2, 3, ..., N In short, we can represent ( 5.3) as
Rdcddsm(k) = Hdcddsm(k)Sdcddsm(k) + Ndcddsm(k) , (5.4)
The same way to detect the signals as distributed SFBC combining with SM with a linear MMSE receiver, the receive signal vector Rdcddsm(k) is multiplied withGM M SE(k), the MMSE detector minimizes the mean square error between actually transmitted symbols and the output of the receiver,
Sˆdcddsm(k) = GM M SE(k)Rdcddsm(k) (5.5)
GM M SEe (k) = (Hdcddsm∗ (k)Hdcddsm(k) + (MtN0
Es)I2Mt)−1Hdcddsm∗ (k), (5.6) where H∗ is the Hermitian transpose of H, the total power transmitted onMtantennas at one symbol time isEsat each base station. We extend SINR calculations in the narrowband
37
MIMO systems [32] to the CDD macro-diversity combining with SM in OFDM system.
Given the equivalent MIMO channelHdcddsm(k) , the per-tone SINR calculation at the sth sub-carrier is given by
SINRM M SEs = Es
MtN0
(Hdcddsm∗ (k)Hdcddsm(k) + (MtN0
Es)I2Mt)−1
s,s
− 1. (5.7)
After the SINR evaluation of MMSE detector, we can apply the EESM approximation method to get the effective SINR ref f from ( 5.7) and then decide the MCS to the same block error rate (BLER) by a lookup table through an AWGN curves as Table 3.1. We can find the throughputs with the decided MCS of the CDD macro-diversity combining with SM scheme.
5.3 Simulation Results
As SFBC macro-diversity combining with SM scheme in Chapter 4, the parameters used in the simulation are listed in Table 4.1. When the mobile station moves from the serving base station (BS 0) to the adjacent base station (BS 1) as shown in Fig. 3.1, we calculate the throughputs for different spatial correlations of the transmit antennas at each base sta-tion and compare with the single base stasta-tion using SM or SFBC. The separasta-tion of each base station is far enough to ignore the spatial correlation between the transmit antennas of BS 0 and BS 1. The main goal of this simulation is to show that the throughput gain can be achieved by adopting CDD macro-diversity combining with SM around the cell edge compared with that of the conventional centralized MIMO-OFDM scheme and SFBC macro-diversity combining with SM scheme. Perfect channel state information at receivers is assumed in our simulations. Particularly, we are interested in the performance compari-son between CDD macro-diversity combining with SM scheme and SFBC macro-diversity combining with SM scheme.
0 250 500 750 1000 1250 1500 0
1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughtputs (bit/s/Hz)
SM 2x2 corr=0.1 SFBC 2x2 corr=0.1 DSFBC−SM 4x2 corr=0.1 DCDD−SM 4x2 corr=0.1
1 bit/s/Hz 1 bit/s/Hz
Figure 5.3: Throughputs of SM 2x2 , SFBC 2x2 , DSFBC-SM 4x2 and DCDD-SM 4x2 withρtx= 0.1 at each BS.
From Fig. 5.3, the poor throughput of the conventional SM scheme around the cell edge is improved obviously by applying the distributed CDD techniques at the adjacent base stations sides. Because CDD can not achieve the optimal diversity gain as SFBC, distributed SFBC outperforms distributed CDD slightly. Using transmit diversity at the base station side increases SINR effectively around the cell edge. It allows us to choose higher order modulation and coding schemes (MCS). Therefore, the CDD macro-diversity combining with SM scheme can also overcome the drawbacks of the single base station using SM and maintain the large throughput for the mobile station around the cell edge. It
39
can be concluded that distributed CDD is another effective scheme to get spatial diversity gain in cellular networks as distributed SFBC.
0 250 500 750 1000 1250 1500
0 1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughtputs (bit/s/Hz)
SM 2x2 corr=0.3 SFBC 2x2 corr=0.3 DSFBC−SM 4x2 corr=0.3 DCDD−SM 4x2 corr=0.3
1 bit/s/Hz 1 bit/s/Hz
Figure 5.4: Throughputs of SM 2x2 , SFBC 2x2 , DSFBC-SM 4x2 and DCDD-SM 4x2 withρtx= 0.3 at each BS.
As Figs. 5.4, 5.5, 5.6 and 5.7 show the effects of increasing spatial correlation of each transmitters for the CDD macro-diversity combining with SM scheme. It is shown that it can provide higher throughput than the conventional SFBC and SM schemes with ρtx = 0.7. The variations of throughputs between CDD macro-diversity combining with SM scheme and SFBC macro-diversity combining with SM scheme are getting small at the cell boundary as spatial correlation increases. Forρtx= 0.9, the throughputs of the two
0 250 500 750 1000 1250 1500 0
1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughtputs (bit/s/Hz)
SM 2x2 corr=0.5 SFBC 2x2 corr=0.5 DSFBC−SM 4x2 corr=0.5 DCDD−SM 4x2 corr=0.5
1 bit/s/Hz
Figure 5.5: Throughputs of SM 2x2 , SFBC 2x2 , DSFBC-SM 4x2 and DCDD-SM 4x2 withρtx= 0.5 at each BS.
schemes are the same at cell boundary.
When the distance between a mobile station and the serving base station is two thirds of the cell radius, Fig. 5.8 shows that the performance of CDD macro-diversity combining with SM scheme is the same as SFBC macro-diversity combining with SM scheme and better than the conventional SFBC and SM schemes with ρtx ≤ 0.3. SFBC macro-diversity combining with SM scheme also outperforms the CDD macro-diversity combining with SM scheme for higher ρtx values. At the cell boundary, Fig. 5.9 shows that the CDD macro-diversity combining with SM scheme performs similar to the SFBC
41
0 250 500 750 1000 1250 1500 0
1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughtputs (bit/s/Hz)
SM 2x2 corr=0.7 SFBC 2x2 corr=0.7 DSFBC−SM 4x2 corr=0.7 DCDD−SM 4x2 corr=0.7
1 bit/s/Hz
Figure 5.6: Throughputs of SM 2x2 , SFBC 2x2 , DSFBC-SM 4x2 and DCDD-SM 4x2 withρtx= 0.7 at each BS.
macro-diversity combining with SM scheme. It can also provide higher throughput than the conventional SFBC and SM schemes.
Table 5.1 shows the increased throughputs of CDD macro-diversity combining with SM scheme compared with the conventional SM scheme. It shows that CDD macro-diversity combining with SM scheme can also effectively improve the throughput under spatially correlated channels and can offer higher data throughput for a mobile station at the cell boundary. Hence, the handover performance can be enhanced greatly.
0 250 500 750 1000 1250 1500 0
1 2 3 4 5 6 7 8 9
Distance from the serving BS (m)
Throughtputs (bit/s/Hz)
SM 2x2 corr=0.9 SFBC 2x2 corr=0.9 DSFBC−SM 4x2 corr=0.9 DCDD−SM 4x2 corr=0.9
0.5 bit/s/Hz
Figure 5.7: Throughputs of SM 2x2 , SFBC 2x2 , DSFBC-SM 4x2 and DCDD-SM 4x2 withρtx= 0.9 at each BS.
Table 5.1: Throughputs Improvement by DCDD-SM Compared with The Conventional SM.
Increased Throughputs
by DCDD-SM (bit/s/Hz) ρtx= 0.1 ρtx= 0.3 ρtx= 0.5 ρtx= 0.7 ρtx= 0.9
Distance from the serving BS 500m (2Rc/3) 1 1 0 0 0
Distance from the serving BS 750m (Rc) 1 1 1 1 0.5
43
0 0.1 0.3 0.5 0.7 0.9 1 0
0.5 1 1.5 2 2.5 3 3.5 4 4.5
Transmit antenna spatial correlation
Throughputs (bit/s/Hz)
SM 2x2 SFBC 2x2 DSFBC−SM 4x2 DCDD−SM 4x2
Figure 5.8: Throughputs of SM 2x2, SFBC 2x2, DSFBC-SM 4x2 and DCDD-SM 4x2 schemes for differentρtxwith the distance from the serving BS equal to 500m (2Rc/3).
0 0.1 0.3 0.5 0.7 0.9 1 0
0.5 1 1.5 2 2.5 3 3.5 4 4.5
Transmit antenna spatial correlation
Throughputs (bit/s/Hz)
SM 2x2 SFBC 2x2 DSFBC−SM 4x2 DCDD−SM 4x2
Figure 5.9: Throughputs of SM 2x2, SFBC 2x2, DSFBC-SM 4x2 and DCDD-SM 4x2 schemes for differentρtxwith the distance from the serving BS equal to 750m (Rc).
45
46
CHAPTER 6
Conclusions
In this thesis, we introduce two new schemes for increasing the throughput of the mobile station around the cell edge of MIMO OFDM cellular mobile networks: space-frequency block code macro-diversity combining with spatial multiplexing scheme and cyclic delay macro-diversity combining with spatial multiplexing scheme. We also evaluate the impacts of transmit antenna spatial correlation. These schemes overcome the drawbacks of the single base station using SM to offer high data rate for the mobile station. However using SM reduces the coverage of base stations. Without increasing the transmit power or the number of antennas of base stations, we apply the transmit diversity at the base station sides to increase the SINR of the mobile station around the cell edge in the downlink case.
The receiver of a mobile station performs diversity combining to increase SINR. By using SM, the serving base station can provide high throughput for the mobile station close to the serving base station, while the mobile station moves to cell area. The two schemes adopt different transmit diversity techniques at the base station sides. The adjacent base stations transmit the same signal to mobile station with cooperation. The mobile station can combine the receive signals to get the spatial diversity gain and increase the SINR around the cell edge in the downlink case. In addition, the impacts of spatial correlation at the transmitter are considered. The performance of SM-MIMO system seriously degreases, especially if there is non-trivial spatial correlation among the transmit antennas. The two schemes are more robust than the conventional SM scheme of the single base station in
spatially-correlated channels.
An EESM-based approach is proposed to evaluate the system-level performance.
The simulation results in Chapters 4 and 5 show that the two macro-diversity schemes can significantly improve performance compared with the conventional SM scheme of the single base station around the cell edge. Distributed SFBC outperforms distributed CDD slightly under low spatially correlated channels. The variations of throughputs increasing between CDD combining with SM scheme and SFBC combining with SM scheme are get-ting small at the outer cell area in high spatially-correlated channels. The throughput of the two schemes are about the same at the cell boundary with ρtx = 0.9. Their performances are quite similar. While the increased throughput satisfies the requirement of mobile sta-tions around the cell edge, there will be no call-dropped case occurred at the cell boundary.
This decreases handover frequency and relieves the ping-pong effect [12]. The handover performance in OFDM cellular networks is also enhanced greatly.
To get the improvement of throughput, more complicated decoders are required for SFBC. In contrast to SFBC diversity combining with SM scheme, CDD macro-diversity combining with SM scheme can be designed for arbitrary base stations and no modification at the receiver is necessary. Due to the quite similar improvement, lower com-plexity and compatibility to existing wireless communication systems, we prefer to adopt the CDD macro-diversity combining with SM scheme instead of SFBC macro-diversity
To get the improvement of throughput, more complicated decoders are required for SFBC. In contrast to SFBC diversity combining with SM scheme, CDD macro-diversity combining with SM scheme can be designed for arbitrary base stations and no modification at the receiver is necessary. Due to the quite similar improvement, lower com-plexity and compatibility to existing wireless communication systems, we prefer to adopt the CDD macro-diversity combining with SM scheme instead of SFBC macro-diversity