3.4 Numerical Results
3.4.5 Multiple Services
In this subsection, we evaluate the performance of the system outage probability of the LP-DCA scheme compared with other dynamic channel assignment schemes in the multiple services environment. Each service class has its requirements of radio resource and the bit energy-to-noise ratio, and different traffic asymmetry of different service classes is imposed on the system. The performance of outage probability significantly impacts on the transmission quality of each service class. In TDD mode, cross-slot co-channel interference is a dominant factor to cause severe performance degradation. A well designed DCA scheme to address the different traffic asymmetry among the virtual cell will effectively improve the performance of system outage probability. Three different classes of service are assumed in the system and the setting of the parameters for these service classes are shown in Table 3.6. Service class 1 is the traditional voice service with balanced traffic load in the downlink and uplink. Service class 2 has the heavy traffic load in the downlink and called downlink enhancement data service. And Service class 3 has the heavy traffic load in the uplink and called uplink enhancement data service. The data service has the lower target NEb0 than the voice service due to advanced error correction coding schemes. The system outage probability in the multiple service environment is defined by
Psys,o=X
is the traffic ratio of class k.
The performance of system outage probability is mainly affected by two factors:
the distribution of the traffic asymmetry of all sectors in a virtual cell and the traffic load of each sector. To evaluate the performance of outage probability with respect to traffic asymmetry, a scenario is adopted to vary the traffic ratios φ to adjust the
Table 3.6: Multiple Services Parameters.
Required Radio Resource Required Radio Resource
in the Downlink in the Uplink Target ENb0
Service Class 1 1 RU 1 RU 4 dB
Service Class 2 5 RU 1 RU 1 dB
Service Class 3 1 RU 5 RU 1 dB
Table 3.7: The Distribution of Traffic Load of three services in each sector.
Sector A Sector B Sector C φ1 φ2 : φ3 φ1 φ2 : φ3 φ1 φ2 : φ3 Step 1 1/3 1 : 1 1/3 1 : 1 1/3 1 : 1 Step 2 1/3 1 : 1 1/3 3 : 2 1/3 2 : 3 Step 3 1/3 1 : 1 1/3 3 : 1 1/3 1 : 3 Step 4 1/3 1 : 1 1/3 9 : 1 1/3 1 : 9 Step 5 1/3 1 : 1 1/3 1 : 0 1/3 0 : 1
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distribution of the traffic asymmetry with a fixed total number of users. In each sector, the number of voice users is fixed to be one-third (φ1 = 1/3) of the total number of users. Initially, the traffic ratios for downlink and uplink enhancement service, φ2 and φ3, are set to be φ2 = φ3 = 1/3, too. In the following steps, we gradually change some users in sector B from the downlink enhancement data service to the uplink enhancement data service for each time. Conversely, we gradually reduce some users in sector C from the uplink enhancement data service to the downlink enhancement data service at the same time. And the traffic ratios φ2 and φ3 of sector A are all fixed 1/3. Table 3.7 illustrates the traffic load condition in the following results.
Figure 3.7, 3.8, and 3.9 illustrate the system outage probability with respect to the traffic asymmetry with given total number of users by 60, 75, and 90. Here, the traffic asymmetry is defined as the difference of the traffic load in downlink between sector B and sector C that is set according to the above description. While the user density is light as shown in Fig. 3.7, the proper outage probabilities of each DCA schemes are below 6% in most traffic asymmetry condition. When the user density increases as shown in Fig. 3.8 and Fig. 3.9, the outage probabilities of LP-DCA are always the lowest one as the traffic asymmetry is increased, while the outage probabilities of random, ordered, and region-based DCA schemes are higher than that of LD-DCA by 5%, 4%, and 2.5% , respectively. It can be also found in Fig.
3.7, Fig. 3.8, and Fig. 3.9 that the increase of the additional outage probability of LP-DCA is limited below 6% as the traffic asymmetry is increased, while the increase of the additional outage probability of the random, ordered, and region-based DCA schemes are about 11.5%, 11%, and 9%, respectively. This is because that LP-DCA can minimize the total receive interference flexibly. The proposed LP-DCA scheme can attain the better system performance and alleviate the cross-slot interference due to the increasing traffic asymmetry. From the above numerical results, the proposed
1 1.5 2 2.5 3 3.5 4 4.5 5 0
1 2 3 4 5 6 7
The corresponding steps with different traffic asymmetry in Table VII
Outage Probability (%)
Random Order Region LP
Figure 3.7: The Outage Probability corresponds to the different kinds of traffic asym-metry when there are 60 users in each sector.
and support asymmetric traffic services effectively.
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1 1.5 2 2.5 3 3.5 4 4.5 5 0
2 4 6 8 10 12
The corresponding steps with different traffic asymmetry in Table VII
Outage Probability (%)
Random Order Region LP
Figure 3.8: The Outage Probability corresponds to the different kinds of traffic asym-metry when there are 75 users in each sector.
1 1.5 2 2.5 3 3.5 4 4.5 5 2
4 6 8 10 12 14 16
The corresponding steps with different traffic asymmetry in Table VII
Outage Probability (γ>abscissa) (%)
Random Order Region LP
Figure 3.9: The Outage Probability corresponds to the different kinds of traffic asym-metry when there are 90 users in each sector.
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CHAPTER 4
Joint Cross-Slot Interference-Based Dynamic Channel Assignment and
Antenna Beamforming for the
TDD/CDMA Systems with Asymmetric Traffic
As the requirement for the asymmetric data services is growing, the time division duplex/code division multiple access (TDD/CDMA) system has been considered as an important wireless network in the future. However, different asymmetric traffic loads among cells may cause heavy cross-slot interference, which can seriously degrade the system performance. To alleviate the impact of the cross-slot interference and improve the system performance, we propose a cross-slot interference-based dynamic channel assignment algorithm incorporated with antenna beamforming techniques.
The proposed cross-slot interference-based DCA algorithm aims to reduce downlink cross-slot interference and distributedly assign downlink and uplink time slots to support asymmetric traffic services in each cell. The antenna beamforming techniques adopted here are mainly to avoid the impact of heavy uplink cross-slot interference.
based DCA algorithm and antenna beamforming can effectively suppress the cross-slot interference in both downlink and uplink, thereby enabling a TDD/CDMA system to flexibly provide various asymmetric traffic loads in different cells and achieve high system performance.
4.1 System Model
In this section, we introduce the cellular system model and the propagation model.
We consider a TDD/CDMA hexagonal cellular system and the mobiles are assumed to be uniformly distributed over the system. It is assumed that power control is conducted in both the downlink and the uplink transmission.