**5.2. Performance comparison of the multiuser schemes**

This section compares the performance achieved by the proposed multiuser MAC schemes, Mu-Basic, Mu-Opportunistic and Mu-Threshold. Figures 13 and 14 plot the throughput and mean total delay performance for the four channel models, *N* = 10 users and a packet size of *L* = 2312 bytes. The performance of the non-realistic Mu-Ideal scheme is also depicted, as a reference of the upper bound that corresponds to the considered scenarios. Mu-Ideal is an ideal opportunistic multiuser scheme in which the users with the highest SNIR values are scheduled on each beam. In other words, the same scheduling objective as in the Mu-Opportunistic scheme (Section 4.2) is targeted. The difference is that, in the Mu-Ideal scheme it has been assumed that the AP has a perfect knowledge of the channel condition and can select the best set of users without any additional overhead. Clearly, this scheme is not practical, since some mechanism for the CSI acquisition must be available at the AP.

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throughput is concerned, to transmit fewer packets but with a higher rate that to transmit in

*ChA* 24 2 49.9 *ChB* 24 3 29.2 *ChC* 36 3 30.7 *ChD* 48 3 23.9

The performance of the two opportunistic schemes, Mu-Opportunistic and Mu-Threshold, is bound between Mu-Basic and Mu-Ideal schemes. To illustrate this point, two performance statistics have been calculated in Table 5. The first metric reflects the throughput gain of the two schemes with respect to the Mu-Basic algorithm. It can be observed that both schemes improve performance under all the considered channel models by scheduling users with high available transmission rates. However, the exact value of the achieved gain depends on the channel quality. For harsh channels, the improvement is more pronounced. In the case of *ChA*, for instance, a gain of approximately 66 % and 99 % is obtained by Mu-Opportunistic and Mu-Threshold, respectively. On the other hand, when the channel quality is good, as in *ChD*, the need for opportunistic scheduling is less critical. Nevertheless, even in that case, an

> **Throughput gain (%) Improvement margin (%) with respect to Mu-Basic with respect to Mu-Ideal**

*Channel Mu-Opport. Mu-Thres. Mu-Opport. Mu-Thres. ChA* 65.84 99.21 26.80 5.56 *ChB* 75.76 94.36 35.64 22.66 *ChC* 49.67 62.69 47.03 35.25 *ChD* 17.25 23.47 65.45 57.11

Mu-Opportunistic and Mu-Threshold are two efficient multiuser schemes but there is still a margin for improvement in order to achieve the upper bound set by the Mu-Ideal. The second metric presented in Table 5 refers to the the available improvement margin. The three schemes share the principle of opportunistic scheduling, but implement it in different ways. Mu-Ideal assumes perfect CSI knowledge without any additional overhead cost, which is an assumption that does not hold for realistic schemes. Mu-Opportunistic introduces considerable overhead since *N* = 10 CTS packets are sent in each transmission sequence. Finally, Mu-Threshold manages to reduce overhead by employing *m* control slots, with *m* usually much smaller than the number of total users *N* (in the presented example, the best

**Threshold Slots Empty Frames** *rγ m* **%**

Multiuser MAC Schemes for High-Throughput IEEE 802.11n/ac WLANs

every transmit sequence with lower rates.

**Table 4.** Best configuration for Mu-Threshold scheme

enhancement of more than 20 % can be achieved.

**Table 5.** Performance statistics for the proposed multiuser schemes

**Channel**

**Figure 13.** Throughput performance comparison

**Figure 14.** End-to-End delay performance comparison

The presented results for the Mu-Threshold have been obtained by considering the best combination of threshold and CTS slot number values. These optimum parameters are summarized in Table 4. In general, the channel statistics influence heavily the Mu-Threshold performance and the optimization of the algorithm is not straightforward since different objectives must be met to maximize performance in diverse scenarios. This can be better understood by examining the percentage of empty frames, given in the last column of the table. In the case of *ChD*, this percentage is low, meaning that the majority of frames feature single or double data transmissions. On the other hand, for harsh channels the minislot-threshold combination that maximizes throughput may result to a higher number of empty frames (even up to 50% for *ChA*), thus revealing that it is more efficient, as far as


throughput is concerned, to transmit fewer packets but with a higher rate that to transmit in every transmit sequence with lower rates.

**Table 4.** Best configuration for Mu-Threshold scheme

20 Recent Trends in Multiuser MIMO Communications

20

10

0

**Figure 13.** Throughput performance comparison

10

0

**Figure 14.** End-to-End delay performance comparison

20

End-to-End Delay (ms)

Mean E

30

30

hroughput (Mbps)

Th

40

50

Mu-Basic Mu-Opportunistic Mu-Threshold Mu-Ideal

Performance close to ideal for harsh channels

L=2312 bytes

Margin for improvement for good channels

Ch\_A Ch\_B Ch\_C Ch\_D

Channel Model

Ch\_A Ch\_B Ch\_C Ch\_D

Channel Model

The presented results for the Mu-Threshold have been obtained by considering the best combination of threshold and CTS slot number values. These optimum parameters are summarized in Table 4. In general, the channel statistics influence heavily the Mu-Threshold performance and the optimization of the algorithm is not straightforward since different objectives must be met to maximize performance in diverse scenarios. This can be better understood by examining the percentage of empty frames, given in the last column of the table. In the case of *ChD*, this percentage is low, meaning that the majority of frames feature single or double data transmissions. On the other hand, for harsh channels the minislot-threshold combination that maximizes throughput may result to a higher number of empty frames (even up to 50% for *ChA*), thus revealing that it is more efficient, as far as

L=2312 bytes

Mu-Basic Mu-Opportunistic Mu-Threshold Mu-Ideal

The performance of the two opportunistic schemes, Mu-Opportunistic and Mu-Threshold, is bound between Mu-Basic and Mu-Ideal schemes. To illustrate this point, two performance statistics have been calculated in Table 5. The first metric reflects the throughput gain of the two schemes with respect to the Mu-Basic algorithm. It can be observed that both schemes improve performance under all the considered channel models by scheduling users with high available transmission rates. However, the exact value of the achieved gain depends on the channel quality. For harsh channels, the improvement is more pronounced. In the case of *ChA*, for instance, a gain of approximately 66 % and 99 % is obtained by Mu-Opportunistic and Mu-Threshold, respectively. On the other hand, when the channel quality is good, as in *ChD*, the need for opportunistic scheduling is less critical. Nevertheless, even in that case, an enhancement of more than 20 % can be achieved.


**Table 5.** Performance statistics for the proposed multiuser schemes

Mu-Opportunistic and Mu-Threshold are two efficient multiuser schemes but there is still a margin for improvement in order to achieve the upper bound set by the Mu-Ideal. The second metric presented in Table 5 refers to the the available improvement margin. The three schemes share the principle of opportunistic scheduling, but implement it in different ways. Mu-Ideal assumes perfect CSI knowledge without any additional overhead cost, which is an assumption that does not hold for realistic schemes. Mu-Opportunistic introduces considerable overhead since *N* = 10 CTS packets are sent in each transmission sequence. Finally, Mu-Threshold manages to reduce overhead by employing *m* control slots, with *m* usually much smaller than the number of total users *N* (in the presented example, the best performance throughput has been obtained for no more than *m* = 3 slots). As a result, Mu-Threshold is closer to the Mu-Ideal.

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For instance, in the case of *ChD*, the optimum performance is achieved for a threshold of 48 Mbps, meaning that all transmissions have taken place at the rates of 48 and 54 Mbps, thus increasing the average transmission rate. On the other hand, since the average user rate for this channel is 36 Mbps, there is a high possibility that users may not satisfy the threshold condition, resulting to empty frames with no data transmissions. For the best configuration of Mu-Threshold for *ChD*, the percentage of empty frames is approximately 24% of the total

Multiuser MAC Schemes for High-Throughput IEEE 802.11n/ac WLANs

So far, a relatively small number of users, *N* = 10 has been considered. The following set of plots in Figure 15 shows the maximum throughput obtained by Mu-Opportunistic and Mu-Threshold as a function of the number of system users *N* for channel models *ChA* and *ChD*. The best configuration for the Mu-Threshold has been considered and the employed

In the case of Mu-Opportunistic, throughput decreases as the number of users grows. This is an unavoidable consequence of the control overhead required for the acquisition of CSI by all the system users. The degradation is more pronounced as the channel improves (e.g., *ChD*) and higher rates are employed for the data transmission (but not the overhead that is sent at the lowest supported rate). The lesson learned from this observation is that when multiple users are present, the Mu-Opportunistic mechanism is not very efficient. As a more viable alternative, the AP could divide the users in smaller groups and poll a user subset in each transmission sequence. This would reduce the multiuser gain but would also limit the

On the other hand, Mu-Threshold handles multiple users in a more efficient way and throughput is actually improved as the user number increases. Several factors affect this behavior. First, when more users are present, the gain extracted from multiuser diversity also increases, since there is a higher probability of assigning a high-user rate on each beam. Second, unlike the Mu-Opportunistic scheme, the control overhead does not increase linearly

The selection of the slot number and the rate threshold provides a flexible mechanism to control the number of participating users in each transmission sequence. The best configuration depends on the channel distribution but generally the following principles

• More slots are required as the number of user grows, to reduce the collision probability in the contention window. By observing the best configuration for each case, marked in

• The collision probability can also be reduced by increasing the rate threshold, which results to a smaller number of participating users (but with higher available rates). Again,

This chapter has presented a novel approach for the integration of multiuser capabilities in IEEE 802.11n/ac based WLANs. On one hand, a low-complexity beamforming technique named MOB has been employed at the PHY layer. The main strength of MOB lies in the

Figure 15, it can be said that *m* generally follows an increasing trend.

as the number of users grows, the threshold is progressively raised.

values for the slot number and the rate threshold are also indicated in the figure.

frame sequences, as indicated in the last column of Table 4.

with *N* but depends on the number of CTS slots *m*.

introduced overhead.

hold:

**6. Conclusions**

Another interesting observation is that the two practical schemes are closer to the ideal under worse channel conditions. In the case of *ChA*, for instance, the improvement margin is 26.8 % for Mu-Opportunistic and only 5.6 % for Mu-Threshold (less that 1 Mbps below the upper throughput bound). The gap between the achieved throughput and the ideal performance opens as the channel conditions improve and in the case of *ChD* both schemes have an improvement margin of more than 50 %. This occurs because the overhead information, consisting of control packets transmitted at the lowest rate, has a greater impact on performance when high data rates are employed.

Table 6 gives an estimation of the improvement achieved by exploiting the multiuser diversity. This gain is reflected in the increase of the average data transmission rate compared to the average user rate for each channel model. The average data transmission rate is calculated as the average of the rates employed for the transmission of all data frames. The average user rate is obtained by calculating the average value of the maximum rate at which a user can transmit, if the best beam (i.e., with the higher SNIR) for the particular user is selected. This value depends on the channel model and is indicated in the second column of the table.


**Table 6.** Multiuser diversity gain

In the case of Mu-Basic, the average transmission rate is lower than the average user rate. This is a direct consequence of random scheduling and beam allocation: users may be selected for transmission when their channel quality is low, or they may receive increased interference from other simultaneous transmissions due to the suboptimal beam allocation. Mu-Opportunistic, on the other hand, exploits multiuser diversity by assigning the best user on every beam. As a result, most transmissions take place at rates above the average. In the case of *ChD*, for instance, the transmission rate is 46.7 Mbps whereas the average user rate is limited to 36 Mbps. It should be noted that Mu-Opportunistic yields the same average transmission rate as the Mu-Ideal scheme, since both schemes implement the same scheduling policy. Despite providing the same transmission rate, the throughput performance of Mu-Opportunistic is lower than the ideal, due to the additional control overhead required for the CSI acquisition.

Finally, the maximum transmission rate values are achieved by Mu-Threshold. At first glance, is seems puzzling to obtain rates above those of the Mu-Ideal scheme. Nevertheless, this can be explained with the help of the data presented in Table 4. By imposing a rate threshold, Mu-Threshold scheme controls the minimum rate that can be employed for transmission. For instance, in the case of *ChD*, the optimum performance is achieved for a threshold of 48 Mbps, meaning that all transmissions have taken place at the rates of 48 and 54 Mbps, thus increasing the average transmission rate. On the other hand, since the average user rate for this channel is 36 Mbps, there is a high possibility that users may not satisfy the threshold condition, resulting to empty frames with no data transmissions. For the best configuration of Mu-Threshold for *ChD*, the percentage of empty frames is approximately 24% of the total frame sequences, as indicated in the last column of Table 4.

So far, a relatively small number of users, *N* = 10 has been considered. The following set of plots in Figure 15 shows the maximum throughput obtained by Mu-Opportunistic and Mu-Threshold as a function of the number of system users *N* for channel models *ChA* and *ChD*. The best configuration for the Mu-Threshold has been considered and the employed values for the slot number and the rate threshold are also indicated in the figure.

In the case of Mu-Opportunistic, throughput decreases as the number of users grows. This is an unavoidable consequence of the control overhead required for the acquisition of CSI by all the system users. The degradation is more pronounced as the channel improves (e.g., *ChD*) and higher rates are employed for the data transmission (but not the overhead that is sent at the lowest supported rate). The lesson learned from this observation is that when multiple users are present, the Mu-Opportunistic mechanism is not very efficient. As a more viable alternative, the AP could divide the users in smaller groups and poll a user subset in each transmission sequence. This would reduce the multiuser gain but would also limit the introduced overhead.

On the other hand, Mu-Threshold handles multiple users in a more efficient way and throughput is actually improved as the user number increases. Several factors affect this behavior. First, when more users are present, the gain extracted from multiuser diversity also increases, since there is a higher probability of assigning a high-user rate on each beam. Second, unlike the Mu-Opportunistic scheme, the control overhead does not increase linearly with *N* but depends on the number of CTS slots *m*.

The selection of the slot number and the rate threshold provides a flexible mechanism to control the number of participating users in each transmission sequence. The best configuration depends on the channel distribution but generally the following principles hold:

