3.3 Rate Maximization Problem
3.3.4 Performance Assessment
order of MtNc. Therefore, compared to SBI, SUI has lower complexity due to the lower number of iterations. However, it is expected that SBI will outperform SUI as it benefits from adaptive power allocation.
The variants proposed for PF and MMF differ from each other in the number of required comparisons in each iteration. In the WP greedy algorithm proposed for PF, the user and the chunk are simultaneously selected from allKNc combinations, i.e. the number of com-parison per iteration is aboutKNc, while in the FP greedy algorithm proposed for MMF the user and the chunk are sequentially selected, thus requiring(K +Nc)comparisons.
3.3 Rate Maximization Problem
Table 3.3: Comparison of the different variants of the sub-optimal algorithm for the rate maximization problem in terms of average user throughput, 95%-tile and Jain’s index.
Average [Mbps] 95%-tile [Mbps] Jain’s Index
SBI WP 7.99 0.43 0.41
FP 3.85 2.70 0.98
SUI
WP 5.47 1.15 0.76
FP 3.14 1.90 0.94
DJ-WP 5.34 1.10 0.75
DJ-FP 2.22 1.48 0.96
on the same chunk. In addition to the statistical performance metrics provided in Table 3.3, the corresponding CCDF of user throughput is also reported for each comparison. The CCDF curves can provide a detailed description on the distribution of user throughput.
Comparison between Disjoint and Joint Approaches
In Figure 3.4, the proposed joint approaches are compared with the corresponding dis-joint approaches. For both WP (red curves) and FP (blue curves) variants, the proposed
0 4 8 12 16
0 0.2 0.4 0.6 0.8 1
x: user throughput [Mbps]
CCDF(x)
SUI−FP SUI−WP SUI−DJ−FP SUI−DJ−WP
FP WP
Figure 3.4: Comparison between joint and DJ approaches.
joint approach achieves higher throughput than the DJ one everywhere in the CCDF. The performance loss of DJ-FP (blue dashed curve) is more significant than that of SUI-DJ-WP (red dashed curve). In order to guarantee MMF, SUI-DJ-FP actually selects in each iteration the chunk and user without considering the channel conditions, therefore, the multi-user diversity gain can not be exploited, which results in significant performance loss compared to the joint approach.
Comparison between PF and MMF User Fairness Strategies
In Figure 3.5, the CCDF curves of the user throughput achieved by SUI-FP and SUI-WP are plotted. By following MMF strategy, the FP variant (red dashed curve) actually sacrifices
0 4 8 12 16
0 0.2 0.4 0.6 0.8 1
x: user throughput [Mbps]
CCDF(x)
SUI−FP SUI−WP
Figure 3.5: Comparison between WP and FP, SUI.
the cell throughput in return for fairness. As expected, the average user throughput of FP is less than that of WP, but the Jain’s index of FP is much closer to 1 than that of WP, indicating better fairness, as shown in Table 3.3. Moreover, FP achieves much higher 95-percentile user throughput, which is consistent with the target of the MMF strategy.
Comparison between Adaptive and Equal Power Sharing
In Figure 3.6, the performance of SBI-FP and SUI-FP is compared. SBI enables adap-tive power allocation among the co-located users, while SUI supposes equal power sharing among co-located users. It can be inferred that under the same fairness strategy much higher user throughput is achievable by adaptive power allocation (blue solid curve) com-pared to that by fixed equal power sharing (red dashed curve).
3.3 Rate Maximization Problem
0 4 8 12 16
0 0.2 0.4 0.6 0.8 1
x: user throughput [Mbps]
CCDF(x)
SBI−FP SUI−FP
Figure 3.6: Comparison between SBI and SUI, FP.
Impact of the AMC Scheme
The AMC scheme applied in the simulations presented so far is AMC-Baseline, cf. Sec-tion 2.7. However, as seen in Figure 2.9, Fine achieves higher data rate than AMC-Baseline over the whole SNR region due to more feasible data rates. In Figure 3.7, the per-formance achieved by AMC-Baseline and AMC-Fine is compared for the variants, namely SUI-FP and SBI-FP. As expected, higher data rate is achieved by AMC-Fine (blue dashed
1 2 3 4 5
0 0.2 0.4 0.6 0.8 1
x: user throughput [Mbps]
CCDF(x)
SUI−FP, AMC−Baseline SUI−FP, AMC−Fine SBI−FP, AMC−Baseline SBI−FP, AMC−Fine
Gain due to AMC with finer grid
Gain due to Adaptive power allocation
Figure 3.7: Comparison between AMC-Baseline and AMC-Fine.
curve) with respect to AMC-Baseline (blue solid curve) when SUI is applied. However,
when SBI is applied, the performance achieved by adopting AMC-Fine (red dashed curve) and AMC-Baseline (red solid curve) is almost the same. This can be explained as follows.
On the one hand, in SBI, adaptive power allocation is carried out by always minimizing the transmit power required for a target data rate. On the other hand, as shown in Fig-ure 2.9, given the same target data rate, AMC-Fine requires the same amount of power as AMC-Baseline. The performance loss of SUI-FP compared to SBI-FP when applying AMC-Baseline, i.e. the difference between solid blue curve and solid red curve, comes from two folds: the less feasible data rates in AMC-Baseline and the equal power sharing assumption. While the performance loss caused by the less feasible data rates in AMC-Baseline is quantified by comparing the performance of SUI-FP with AMC-AMC-Baseline and SUI-FP with AMC-Fine, i.e. the gap between the blue solid curve and the blue dashed curve, the performance loss due to the equal power sharing is then quantified by the gap between the blue dashed curve and the red solid curve, as depicted in Figure 3.7.
As summary, following conclusions can be drawn based on the presented results:
• The proposed joint approach generally outperforms the existing DJ approach, espe-cially in the FP variant targeting at max-min fairness.
• Compared to the WP variant targeting at proportional fairness, the FP variant target-ing at max-min fairness sacrifices overall cell throughput for better user fairness.
• Compared to SUI, SBI benefits from adaptive power allocation in the spatial domain and thus achieves higher user throughput.
• Under the assumption of equal power sharing, significant performance increase can be obtained by using an AMC scheme with finer grid, i.e. with more feasible data rates.
• Under the assumption of adaptive power allocation in the spatial domain, almost no performance increase can be obtained by using an AMC scheme with finer grid.
4 Signaling Overhead for Adaptive Resource Allocation
4.1 Introduction
Resource allocation plays an important roll in optimizing the system performance. As stated in Section 2.3, the channel fading varies over frequency band due to multi-path propagation and over time due to mobility. Since different users perceive different channel qualities due to independent fading, a resource with deep fading for one user may still be favorable for the others, referred to as selective channel diversity. Moreover, in cellular systems, the variability of the channel quality in terms of SINR comes from the fading of the signal as well as the fading of the interference from adjacent APs, which is referred to as mutual interference diversity. Besides, data service is generally characterized as burst traffic and the arrival time of data service is also different among users, which leads to independent varying of current user data rate requirements, referred to as traffic diversity.
Thus, resource allocation adaptive to the traffic status can potentially outperform resource allocation with certain fixed bandwidth assignment. In summary, three kinds of multi-user diversities can be identified as selective fading channel diversity, mutual interference diversity and traffic diversity [LL03]. However, any AP attempting to exploit these multi-user diversities would require the knowledge of both channel and traffic conditions, which may cause additional signaling overhead. In general, high multi-user diversity gain can be obtained with precise channel and traffic knowledge, but from the perspective of system performance, the high signaling overhead due to acquisition of precise information will mitigate the achieved performance gain. Therefore, it is important to balance the signaling overhead and the performance gain achieved by adaptive resource allocation.
In this chapter, the following aspects concerning the signaling for adaptive resource al-location are addressed.
• Different sub-carriers undergo varied channel conditions, and so adaptive modulation and coding improves spectrum efficiency by selecting for each sub-carrier a suitable MCS with respect to its SINR value, cf. Section 2.7. Thus, additional downlink control data is required to inform users about the MCS selection for each sub-carrier.
This amount of control data can be reduced by choosing the same MCS for a chunk
consisting of adjacent sub-carriers and symbols at the expense of certain performance degradation. In Section 4.2, the trade-off between the signaling reduction and the performance degradation is illustrated by analytical derivations, based on which a proper chunk dimension can be identified.
• Due to mobility, the channel varies over time and so the channel knowledge shall be measured and/or delivered periodically. In Section 4.3, a semi-analytical method is presented to find the optimum update interval for the channel knowledge, with which the overall system performance is maximized.
• Adaptive resource allocation presented in Chapter 3 is based on the assumption that full CSI is available at the AP and it achieves significant performance gain but also requires considerable channel feedback. Indeed, SDMA can be enabled by other beamforming strategies based on partial CSI. In Section 4.4, two kinds of beamform-ing strategies requirbeamform-ing reduced channel feedback are address and the correspondbeamform-ing performance degradation is illustrated.
• The AP needs the information about traffic conditions such as the amount of data to be transmitted for the optimization of adaptive resource allocation. In downlink, the AP, as the transmitter, knows the traffic conditions. However, in uplink, the AP can only know the traffic conditions via signaling from users. In Section 4.5, the uplink bandwidth request mechanism, which allows users to deliver the traffic condition such as data rate requirements to the AP, is discussed.