4 Link Adaptation for MIMO-Enhanced 802.11a Systems 59
4.3 Modified MAC/PHY Layer Overheads
Figure 3.4 shows the standard IEEE 802.11 frame formats. The DURATION fields in the RTS and CTS frames contain the Drts and Dcts respectively. As the algorithm described above, the modification of the standard IEEE 802.11 frame formats is shown in Figure 4.8. A description of each modification is given next.
1. A new MAC data frame is introduced, shown in Figure 4.8(a), in which the standard IEEE 802.11 data frame has been changed to include a 32-bit check sequence positioned immediately after the SOURCE ADDRESS field. The check sequence is used to protect RSH, which consists of the FRAME CONTRL, DURATION, DESTINATION ADDRESS, and SOURCE ADDRESS fields. Besides, we interchange the BSSID and SEQUENCE CONTROL fields, and include the SEQUENCE CONTROL field in RSH.
The reason for this will be discussed in the following description.
2. The RTS and CTS control frames, shown in Figure 4.8(b) and (c), have been changed to encode a 1-bit MIMO subfield, 3-bit RATE subfield, and 12-bit LENGTH subfield, in place of the 16-bit DURATION field in the standard IEEE 802.11 frames. The contents of the MIMO and RATE subfields are listed in Table 4.1 and 4.2. The LENGTH subfield gives the size of the data frame in octets.
3. The PPDU format, shown in Figure 4.9, has to adopt the modification in MAC data frames. The functionality of RSH is the same as the RTS frame.
Thus, it should be transmitted by the most robust transmission mode, i.e. the BPSK modulation and rate-1/2 convolutional coding. We can see that the SERVICE field and reservation subheader together adds up to eight OFDM
symbols, that’s why we interchange the BSSID and SEQUENCE CONTROL fields and include the sequence control fields in RSH.
Recall that, the use of the RTS/CTS exchange is to estimate the channel matrix between the transmitter and receiver. To achieve this purpose, the RTS frame must be transmitted by MIMO techniques [35][36]. Therefore, we sent the RTS frame, CTS frame, ACK frame, and PLCP with RSH are sent by STBC to increase the transmission reliability.
According to the modification described above, to transmit a frame with an l-octet
data payload over the STBC-enhanced 802.11a system using PHY mode m, the transmission duration is
data PREAMBLE SIGNAL SERVICE RSH SYM
T l m T T T l T
On the other hand, over the VBLAST-enhanced 802.11a system, the transmission duration with PHY mode m is
( )
data PREAMBLE SIGNAL SERVICE RSH SYM
n l n
Note that, the coded data is split into two sub-streams transmitted in parallel via two transmit antennas. Similarly, the transmission duration for an RTS frame, a CTS frame, and an ACK frame are the same as (3.13), (3.14), and (3.15) respectively because the MAC frame sizes are unchanged and STBC has no improvement in data rate.
Therefore, we can compute the effective goodput in MIMO systems, as in Section 3.4, with the modified transmission durations for different frames in this section and error performances of different transmission strategies in Section 4.1.2 and 4.1.3.
Using this result as the selection metric in Section 4.2, we complete the whole link adaptation algorithm. The following section shows the computer simulations.
4.4 Computer Simulations
At first, we try to find some relation between the squared Frobenius norm and minimum singular value, the main factor for determining the performances of STBC and VBLST, of the MIMO channel matrix. We obtain 20 realizations of independent flat Rayleigh fading 2x2 MIMO channel matrix, and find their squared Frobenius norm and minimum singular value respectively. The results given in Figure 4.10 show that these two parameters of the MIMO channel have no obvious correlation between each other. Therefore, the switching threshold between STBC and VBLAST may differ with every independent MIMO channel matrix (see Table 4.3 and 4.4). Considering nodes A and B in Figure 4.10, node A represents the channel matrix
and node B represents the channel
matrix . They have almost the
same squared Frobenius norm (about mean squared Frobenius norm, 4) but quite different minimum singular value. We take these two MIMO channel realizations to compute the effective goodput of STBC and VBLAST enhanced 802.11a systems with MSDU size of 2,000 octets and five contending stations, and the comparison results are listed in Table 4.3 and 4.4. As expected, the STBC is chosen in the low SNR range while VBLAST is preferred in the high SNR range for their underlying functionalities.
The resulting maximum goodputs based on the adaptive PHY mode and MIMO coding 0.0079513-0.69987i 0.56974+0.20471i
selection listed in Table 4.3 and 4.4 are depicted in Figure 4.11. Note that, since the squared Frobenius norms of both channel matrices are almost the same, the performances in the low SNR range are similar due to the same operating MIMO coding, STBC. But the difference between the minimum singular values results in a great performance gap in the high SNR range. The rationale behind this is that, the VBLAST which provides high spectral efficiency experiences fewer errors with the bigger minimum singular value. In other words, the MIMO system whose channel matrix has a larger minimum singular value utilizes the spectrum more efficiently.
Compared to the standard IEEE 802.11a system with the RTS/CTS access method discussed in Chapter 3, assuming that the instantaneous channel gain is equal to
{ }
i j, 2 1E h = , the MIMO enhanced 802.11a systems indeed improve the goodput as shown in Figure 4.12. In the SNR range from 24 to 34 dB, the MIMO enhanced 802.11a system with channel matrix H2 loses its advantage due to the modified data frame format. In this range, it chooses the PHY mode 8 as does the standard IEEE 802.11a system. But the modified data frame format consists of eight OFDM symbols (including the SERVICE and RSH fields) transmitted with the lowest PHY mode, which is transmitted with the highest PHY mode in the standard IEEE 802.11a system in the mentioned SNR range. Therefore, it incurs additional transmission time for overheads, which degrades the resulting goodput. Fortunately, the influence is negligibly small.
Another observation is that VBLAST increases only about 20% maximum goodput of the IEEE 802.11a WLAN. It might conflict with the twice data rate VBLAST provides. However, the increased data rate is only beneficial for MPDU.
Other PHY overheads and a part of MAC headers are transmitted with the original data rate. Using (4.23) and (4.24), we can show the transmission duration for both STBC
and VBLAST with 2,000 octets and PHY mode 8 in the best scenario, i.e. no errors occur during the progress of the transmission, as follows.
( )
bkoff rts cts data ack
T T T T T tSIFSTime tDIFSTime
s
The corresponding goodput is
( )
Therefore, we know that VBLAST can only increase about 30% goodput compared to STBC at best due to the unavoidable MAC/PHY overheads.
4.5 Summary
In this chapter, we apply the MIMO coding techniques, STBC and VBLAST, to further enhance the goodput of the IEEE 802.11a system. Since the performances of STBC and VBLAST depend on different characteristics of the MIMO channel matrix.
We have to adopt the RTS/CTS exchange to obtain the complete MIMO channel information and modify RTS, CTS, and data frame formats to carry the information for adaptive selection. The simulation results show that the MIMO system whose channel matrix has a larger minimum singular value utilizes the spectrum more efficiently. We
also compare the MIMO-enhanced 802.11a systems with standard IEEE 802.11a system using RTS/CTS access method. As expected, the MIMO coding techniques indeed upgrade the goodput of the IEEE 802.11a system.
1k
Figure 4.1: Linear combining for detection of STBC at kth subcarrier in 2x2
MIMO-OFDM systems
Figure 4.2: Equivalent maximum ration combining model for STBC at kth subcarrier in 2x2 MIMO-OFDM systems
k
x
i2
H
k Fn
kiki
y
( )
Es2Figure 4.3: Equivalent scaled AWGN channel model for STBC at kth subcarrier in 2x2 MIMO-OFDM systems
0 5 10 15 20 25 30
10-20 10-15 10-10 10-5 100
SNR (dB)
BER
Mode 1 Mode 2 Mode 3 Mode 4 Mode 5 Mode 6 Mode 7 Mode 8
Figure 4.4: Instantaneous upper bound BER performance of eight PHY modes of STBC-enhanced 802.11a versus SNR (Es/N0)
Information
Figure 4.5: VBLAST architecture at kth subcarrier in 2x2 MIMO-OFDM systems
0 5 10 15
Figure 4.6: Instantaneous simulated and upper bound BER of VBLAST system with QPSK modulation versus SNR (Es/N0)
5 10 15 20 25 30
Figure 4.7: Instantaneous upper bound BER performance of eight PHY modes of VBLAST-enhanced 802.11a versus SNR (Es/N0)
E (RATE is indicated in SIGNAL) E (RATE is indicated in SIGNAL) RAT
RAT 4 bi 4 bi
Figure 4.9: Modified PPDU frame format of IEEE 802.11a OFDM PHY for link adaptation
Figure 4.10: Distribution of squared Frobenius norm and minimum singular value generated by 20 independent flat Rayleigh fading 2x2 MIMO channel matrix
0 5 10 15 20 25 30 35 40 45 0
5 10 15 20 25 30
SNR (dB)
Goodput (Mbps)
H1 H2
Figure 4.11: Maximum goodput for adaptive PHY mode and MIMO coding for 2x2 MIMO-enhanced 802.11a systems
0 5 10 15 20 25 30 35 40 45
0 5 10 15 20 25 30
SNR (dB)
Goodput (Mbps)
MIMO enhanced 802.11a (H1) MIMO enhanced 802.11a (H2) 802.11a
Figure 4.12: Maximum goodupt for adaptive PHY mode and MIMO coding for 2x2 MIMO-enhanced 802.11a systems and standard IEEE 802.11a system
with h =2 1
Table 4.1: Contents of MIMO subfield in modified 802.11 MAC frame formats
MIMO Scheme Contents
STBC 0 BLAST 1
Table 4.2: Contents of RATE subfield in modified 802.11 MAC frame formats
Rate (Mbps) Contents
6 000 9 001 12 010 18 011 24 100 36 101 48 110 54 111
Table 4.3: Adaptive PHY mode and MIMO coding selection for 2x2 channel matrix with 2,000 MSDU and five contending stations. Deep color represents STBC while the light one stands for VBLAST
2
4.0822, min 0.85341
F = λ =
H
Mode 1 Mode 3 Mode 4 Mode 3 Mode 4 Mode 5 Mode 6 Mode 7 Mode 8 SNR (dB) ~4 4~8 8~10 10~13 13~15 15~19 19~23 23~26 26~
Table 4.4: Adaptive PHY mode and MIMO coding selection for 2x2 channel matrix with 2,000 MSDU and five contending stations. Deep color represents STBC while the light one stands for VBLAST
.9888 0.15856
2
3 , min
F = λ =
H
Mode 1 Mode 3 Mode 4 Mode 5 Mode 6 Mode 7 Mode 8 Mode 6 Mode 7 Mode 8 SNR (dB) ~5 5~8 8~11 11~14 14~19 19~21 21~34 34~38 38~41 41~
Chapter 5 Conclusion
In this thesis, the goodput performance is derived analytically for peer-to-peer communication with numbers of contending stations in the IEEE 802.11a DCF system.
The effective goodput is expressed as a closed-form function of the data payload length, wireless channel condition, number of contending stations, and selected transmission rate for both the basic access method and RTS/CTS method. Assuming the availability of the wireless channel conditions and number of contending stations, we propose a link adaptation scheme which selects the best combination of the PHY mode and MAC mechanism based on the theoretical analysis. For instance, the higher rate PHY mode is chosen for good reception condition (high SNR value) because of its better goodput performance compared to the lower rate PHY mode and vice versa. But the selection of the MAC mechanisms requires more detailed information in addition to the channel condition. With regards to the data payload length, the longer it is the more demands of the RTS/CTS access method is required. The concept behind this fully agrees with the idea of using RTS_Threshold, the only parameter to decide as to whether the RTS/CTS access method is applied in former researches [2][3]. However, for the same data payload length, the use of the basic access method is preferable over the high-quality channel with higher rate PHY mode. Needless to say, the transmission duration of the data frame is the primary concern not the length. On the other hand, the
more contending stations in the network, the more potential collision events occur, thus the more likely the RTS/CTS access method is chosen.
Above-mentioned link adaptation scheme utilizes the MAC and PHY layers for achieving the optimum goodput in the inherent IEEE 802.11a WLANs. To further increase the link goodput, we introduce MIMO coding techniques, STBC and VBLAST, in Chapter 4. Through the performance analyses for both techniques, STBC gives an SNR gain (related to the squared Frobenius norm of the MIMO channel matrix) to enhance link reliability while VBLAST provides spectrum efficiency at the cost of poor error performance (related to the minimum singular value of the MIMO channel matrix). The RTS/CTS exchange is adopted to obtain complete channel information and perform link adaptation which selects the best combination of the MIMO coding technique and the PHY mode. The simulation results show that the MIMO system whose channel matrix has a larger minimum singular value utilizes the spectrum more efficiently since the VBLAST, which provides high spectrum efficiency, experiences fewer errors. We also compare the MIMO enhanced 802.11a systems with standard IEEE 802.11a system using the RTS/CTS access method. As expected, the MIMO coding techniques indeed upgrade the goodput of the 802.11a system.
The evaluated performance is based on the upper bound of error performance for VBLAST. Therefore, we underestimate the MIMO-enhanced 802.11a system to some degree. A more accurate performance analysis on VBLAST is a worthy research topic that can provide deeper insight and comprehensive understanding of the VBLAST-enhanced 802.11a. From another point of view on the MAC layer, the unavoidable overheads restrict the maximum goodput of the 802.11a WLANs. The ideas of frame aggregation and flow control can reduce the impact effectively. The frame aggregation scheme gathers several small data frames into one bigger data frame to reduce the amount of transmitted overheads. With the flow control scheme, nodes
with good channel conditions transmit the aggregated frames in burst while the nodes with bad channel condition buffer their data frames and perform frame aggregation.
However, the efforts for increasing the goodput for peer-to-peer communication may be in vain in the multihop scenarios unless routing protocols are able to take into account different channel conditions and transmission strategies. Because current routing protocols derive routes based on hop counts, the nodes farther apart with a poor channel condition communicate with each other at a lower data rate, thus poorer performance. Therefore, our future works shall include advanced subjects at PHY, MAC, and Network layers.
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