4 Cross-Layer Protection Strategies for AMC Over IEEE
5.3 Transmission Mode Selection Strategies
5.3.2 Analysis of IEEE 802.11n Channel Model
In this section we determine some of the important properties of the 802.11n channel model. The time-domain MIMO channel matrix can be obtained from the Matlab program in [50], with proper antenna correlation properties. For the TGn channel model, there may be only several significant eigen channels available. Hence, in the following simulations, we use the information theoretic criterion [50] such as the AIC (Akaike Information Criterion) [50] or the MDL (Minimum Description Length) [50] to decide the number of effective eigen channels for the beamforming mode. The two popular information criteria are given as follows:
( )
(
( ) ( ))
( )where Ns is the number of effective eigen channels and M is the number of antenna elements. The effective eigen channel number estimates are given by
Figures 5.6 (a)-5.11 (a) show the ergodic capacity for Models A-F assuming a 4x4 MIMO system and the LOS conditions. Each simulation result is obtained by the average from 10000 independent trials. The results indicate that the ergodic capacity of V-BLAST is much larger than adaptive array in model A. In the other models, the
ergodic capacities of adaptive array are higher than that of V-BLAST.
The same simulations are repeated for the NLOS conditions. Figures 5.6 (b)-5.11 (b) show the ergodic capacity plot for two types of the space-time processing techniques corresponding to the LOS conditions. The results indicate that the ergodic capacity of V-BLAST is much larger than adaptive array in models A, D, E, and F. In models B and C, the ergodic capacity of adaptive array is higher than that of V-BLAST.
For models D, E and F, it is expected that the capacity is higher because of more clusters present with wider angular spread when compared to models A, B, and C.
5.4 Summary
In this chapter, we first use the condition number to classify channels. The channel with a larger condition number is considered as a CLR channel; otherwise, it will be classified into a UHR channel. Optimal space-time processing is then determined based on the channel condition. Further, in the case of UHR channel, we will select V-BLAST.
In addition, over a CLR channel, we will select beamforming with adaptive modulation.
MIMO
Figure 5.1: MIMO techniques and their benefits.
-50 0 50 100 150 200 250 300 350 400
Figure 5.2: Model D delay profile with cluster extension (overlapping clusters).
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0
100 200 300 400 500 600 700 800 900
1000 Uncorrelated full rank channel,Mt = Mr = 4
Sample Condin nutio
mbevaer lu
(a)
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0
2 4 6 8 10
12x 1033 Correlated low rank channel,Mt=Mr=4
Sample Condin nutio
mbevaer lu
(b)
Figure 5.3: Condition number with MT = MR = 4. (a) Maximum condition number = 603.0125 and mean = 10.8683 over UHR channel. (b) Minimum condition number = 1.9585 × 1016 over CLR channel.
10-1 100 101 102 103 104 105 4.2
4.4 4.6 4.8 5 5.2 5.4 5.6 5.8
K (dB) Erg
odic c apacity ( b/s/
Hz)
Mt = Mr = 2, SNR = 10 (dB)
SVD-Beamforming V-BLAST
(a)
105-1 100 101 102 103 104 105
6 7 8 9 10 11 12
K (dB) Eodapacib/s/rg cty (ic
Hz)
Mt = Mr = 4, SNR = 10 (dB)
SVD-Beamforming V-BLAST
(b)
Figure 5.4: Ergodic capacity versus Ricean K–factor of various transmission techniques and average SNR = 10 dB. (a) MT = MR = 2. (b) MT = MR = 4.
0 5 10 15 20 25 30 0
5 10 15 20 25 30 35
SNR (dB) Eodapacib/s/rgic cty (
Hz)
MT = MR = 4, K=3 SVD Beamforming
V-BLAST
(a)
0 5 10 15 20 25 30
2 4 6 8 10 12 14
SNR (dB) Eodapacib/s/rg cty (ic
Hz)
MT = MR = 4, K=104
(b)
Figure 5.5: Ergodic capacity versus SNR of various transmission techniques with MT = MR = 4. (a) UHR channel. (K = 10−1) (b) CLR channel. (K = 104)
(a)
(b)
Figure 5.6: Ergodic capacity CDFs of various transmission techniques with MT =
MR = 4. (a) IEEE 802.11n channel. (Model A, LOS condition) (b) IEEE 802.11n channel. (Model A, NLOS condition)
10 15 20 25 30
0 0.2 0.4 0.6 0.8 1
Ergodic capacity (b/s/Hz)
CDF
MT = MR = 4
V-BLAST
Beamforming (Ns=2) V-BLAST (i.i.d. case)
12 14 16 18 20 22 24 26 28
0 0.2 0.4 0.6 0.8 1
Ergodic capacity (b/s/Hz)
CDF
MT = MR = 4
V-BLAST
Beamforming (Ns=2) V-BLAST (i.i.d. case)
5 10 15 20 25 30 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4
V-BLAST
Beamforming (Ns=2) V-BLAST (i.i.d. case)
(a)
8 10 12 14 16 18 20 22 24 26 28
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4
V-BLAST
Beamforming (Ns=2) V-BLAST (i.i.d. case)
(b)
Figure 5.7: Ergodic capacity CDFs of various transmission techniques with MT = MR = 4. (a) IEEE 802.11n channel. (Model B, LOS condition) (b) IEEE 802.11n channel. (Model B, NLOS condition)
5 10 15 20 25 30 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4
V-BLAST
Beamforming (Ns=2) V-BLAST (i.i.d. case)
(a)
5 10 15 20 25 30
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4
V-BLAST
Beamforming (Ns=2) V-BLAST (i.i.d. case)
(b)
Figure 5.8: Ergodic capacity CDFs of various transmission techniques with MT = MR = 4. (a) IEEE 802.11n channel. (Model C, LOS condition) (b) IEEE 802.11n channel. (Model C, NLOS condition)
8 10 12 14 16 18 20 22 24 26 28 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4 V-BLAST
Beamforming Ns=2 V-BLAST (i.i.d. case)
(a)
8 10 12 14 16 18 20 22 24 26 28
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4 Beamforming Ns=2
V-BLAST (i.i.d. case) V-BLAST
(b)
Figure 5.9: Ergodic capacity CDFs of various transmission techniques with MT = MR = 4. (a) IEEE 802.11n channel. (Model D, LOS condition) (b) IEEE 802.11n channel. (Model D, NLOS condition)
8 10 12 14 16 18 20 22 24 26 28 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4
V-BLAST
Beamforming (Ns=2) V-BLAST (i.i.d. case)
(a)
10 12 14 16 18 20 22 24 26 28 30
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4
V-BLAST
Beamforming (Ns=2) V-BLAST (i.i.d. case)
(b)
Figure 5.10: Ergodic capacity CDFs of various transmission techniques with MT = MR = 4. (a) IEEE 802.11n channel. (Model E, LOS condition) (b) IEEE 802.11n channel. (Model E, NLOS condition)
8 10 12 14 16 18 20 22 24 26 28 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4
V-BLAST
Beamforming Ns=2 V-BLAST (i.i.d. case)
(a)
8 10 12 14 16 18 20 22 24 26 28
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ergodic capacity (b/s/Hz) CDF
MT = MR = 4 V-BLAST
Beamforming (Ns=2) V-BLAST (i.i.d. case)
(b)
Figure 5.11: Ergodic capacity CDFs of various transmission techniques with MT = MR = 4. (a) IEEE 802.11n channel. (Model F, LOS condition) (b) IEEE 802.11n channel. (Model F, NLOS condition)
Table 5.1: Summary of model parameters for LOS/NLOS conditions. K-factor for LOS conditions applies only to the first tap, for all other taps K=−∞ dB.
A (optional)
LOS/NLOS 0/ -∞ 0 1 tap
B LOS/NLOS 0 / -∞ 15 2
C LOS/NLOS 0 / -∞ 30 2
D LOS/NLOS 3 / -∞ 50 3
E LOS/NLOS 6 / -∞ 100 4
F LOS/NLOS 6 / -∞ 150 6
Table 5.2: Model to environment mapping.
Environment Condition Model
LOS B – LOS
Residential
NLOS B - NLOS LOS B - LOS Residential/
Small Office NLOS C - NLOS LOS C - LOS Typical
Office NLOS D - NLOS
LOS D - LOS Large Office
NLOS E - NLOS LOS E - LOS Large Space
(Indoors and Outdoors)
NLOS F - NLOS
Chapter 6 Conclusion
In this thesis, we survey the MIMO techniques and explore their applications in the OFDM systems. With the aid of OFDM, we show that MIMO can find a feasible way in extending itself to wideband transmission based on the flat fading channels introduced by OFDM tones. In Chapter 2, we present two possible MIMO-OFDM architectures: one is beamforming based OFDM, aimed to provide high quality communication link and the other is targeted at high spectral efficiency.
In Chapter 3, we compare several configurations with different modulation orders and conclude that the best scheme with respect to BER performance differs in different channel conditions in the adaptive MIMO-OFDM systems. Although the adaptive MIMO-OFDM systems can enjoy both the diversity and multiplexing gains in a flexible manner, it does not consider the requirement of higher layer. By analytical throughput computation, the adaptive MIMO-OFDM systems at physical layer will not lead to a significant throughput increase at higher layers.
Observing that the network performance in mobile wireless applications is determined significantly by a complex interaction between PHY, medium access (MAC), link layer, and transmission control protocol (TCP), a joint design of these layers was proposed in Chapter 4. The cross layer design AMC system combines adaptive modulation and coding scheme at the physical layer and MAC protection
strategies at the MAC layer to maximize the system throughput under prescribed delay and performance constrains. It presents a new point of view in which SM and SD are complementary rather than competing approaches. We also derived a closed-form expression of the average throughput for packets transmission. This expression can be used to find the optimum packet length for a given channel condition to achieve the maximum throughput. Besides, the expression has shown that factors such as the optimum packet length and optimum transmission rate are both functions of the signal to noise ratio. These equations can be used to find the optimum transmission parameters that the system should be operated with in order to achieve the maximum throughput. By adjusting the transmission parameters to prevent ill-conditioned sub-channels from dominating the system performance, an indirect form of diversity is also drawn. Computer simulation result demonstrated the throughput and PER performance improvement of the cross-layer design AMC.
In chapter 5, we consider a wireless communications system with smart antenna and MIMO techniques incorporated. It can select effective techniques to cope with the problems associated with the wireless environment. The condition number is used to determine the channel type. Moreover, from the point-of-view of ergodic capacity, the link-optimal space-time processing technique for a specific channel condition is then discussed. Consequently, in the case of UHR channel, V-BLAST is the primary modes to be considered. With the aid of the information criterion, we can find the effective eigen channel and select beamforming mode over a CLR channel. The simulation results indicate that the ergodic capacity of V-BLAST is much larger than the adaptive array in model A. In the other models, the ergodic capacities of the adaptive array are higher than that of V-BLAST. The ergodic capacity of models D, E, F, are higher because of the more clusters present with wider angular spread when compared to the models A, B, and C.
As a remark for addressing the ability of system extension, the proposed adaptive MIMO transceiver is an example of link adaptation which can be achieved by adjusting the transmission parameters. It is noted that the proposed adaptive MIMO transceiver is essentially an adaptive modulation and coding scheme at the physical layer, and integrated with several protection strategies at the MAC layer to achieve the QoS demands under prescribed delay and error performance constraints. Finally, the assumptions that perfect CSI is available at the receiver, and the feedback channel has zero delay and is error free, may not always hold true. One possible extension of this work is to design and analyze the cross layer design with imperfect CSI at the transmitter. Besides, the impact of cross layer design on other parameters at the physical and higher layers is also worth investigating.
Bibliography
[1] G. J. Foschini, “Layered space-time architecture for wireless communication in a fading environment when using multiple antennas,” Bell Labs Syst. Tech. J., vol. 1, pp. 41-59, Autumn 1996.
[2] G. J. Foschini and M. J. Gans, “On limits of wireless communications in a fading environment when using multiple antennas,” Wireless Personal Commun., vol. 6, no. 3, pp. 311-335, 1998.
[3] P. W. Wolniansky, G. J. Foschini, G. D. Golden, R. A. Valenzuela, “V-BLAST: an 1rchitecture for realizing very high data rates over the rich-scattering wireless channel,” URSI International Symposium, pp. 295-300, 29 Sep. -2 Oct. 1998.
[4] X. Li, H. Huang, G. J. Foschini, and R. A. Valenzuela, “Effects of iterative detection and decoding on the performance of BLAST,” IEEE GLOBECOM, vol. 2, pp. 1061-1066, 2000.
[5] E. Biglieri, G. Taricco and A. Tulino, “Decoding space-time codes with BLAST architectures,” IEEE Trans. Signal Processing, vol. 50, no. 10, pp. 2547-2552, Oct.
2002.
[6] Y. Li, J. H. Winters, N. R. Sollenberger, “MIMO-OFDM for wireless communications: signal detection with enhanced channel estimation,” IEEE Trans.
Commu., vol. 50, no. 9, pp. 1471-1477, Sept. 2002.
[7] S. Catreux, D. Gesbert, V. Ercge, “Adaptive modulation and MIMO coding for broadband wireless data networks,” IEEE Communications Magazine, June 2002.
[8] S. Shim, J. S. Choi, C. Lee, D. H. Youn, “Rank adaptive transmission to improve the detection performance of the BLAST in spatially correlated MIMO channel,”
IEEE VTC 2002-Fall, vol. 1, pp. 195-198, Sept. 2002.
[9] A. Goldsmith and S. Chua, “Variable-rate variable-power MQAM for fading channels,” IEEE Trans. Commu., vol. 45, pp. 1218-1230, Oct. 1997.
[10] W. T. Webb and R. Steele, “Variable rate QAM for mobile radio,” IEEE Trans.
Commu., vol. 43, no. 7, pp. 2223-2230, July 1995.
[11] S. M. Alamouti, “A simple transmitter diversity technique for wireless communications,” IEEE J. Select. Areas Commun., vol. 16, no. 8, pp. 1451-1458, Oct. 1998.
[12] V. Tarokh, N. Seshadri, and A. R. Calderbank, “Space-time codes for high data rate wireless communication: performance analysis and code construction,” IEEE Trans. Inform. Theory, vol. 44, no. 2, pp. 744-765, Mar. 1998.
[13] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space-time block codes from orthogonal designs,” IEEE Trans. Inform. Theory, vol. 45, no. 5, pp. 1456-1467, July 1999.
[14] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space-time block coding for wireless communications: performance results,” IEEE J. Select. Areas Commun., vol. 17, no. 3, pp. 451-460, Mar. 1999
[15] D. Qiao, S. Choi, and K. G. Shin, “Goodput Analysis and Link Adaptation for IEEE 802.11a Wireless LANs,” IEEE Trans. Mobile Comp., vol. 1, no. 4, 2002, pp. 278–92.
[16] P. Ferre, A. Doufexi, A. Nix, D. Bull, “Throughput Analysis of IEEE 802.11 and IEEE 802.11e MAC,” WCNC, 2004.
[17] H. Sampath, S. Talwar, J. Tellado, V. Erceg and A. Paulraj, “A fourth-generation MIMO-OFDM broadband wireless systems: design, performance, and field trial results,” IEEE Communications Magazine, vol. 40, no. 9, pp. 143-149, Sep. 2002.
[18] N. Al-Dhahir, C. Fragouli, A. Stamoulis, W. Younis, and R. Calderbank,
“Space-time processing for broadband wireless access,” IEEE Communications Magazine, vol. 40, no. 9, pp. 136-142, Sep. 2002.
[19] T. H. Liew and L. Hanzo, “Space-time block coded adaptive modulation aided OFDM,” IEEE GLOBECOM, vol. l, pp. 136-140, Nov. 2001.
[20] D. Gesbert, L. Haumonte, H. Bölcskei, R. Krishnamoorthy, A J. Paulraj,
“Technologies and performance for non-line-of-sight broadband wireless access networks,” IEEE Communications Magazine, vol. 40, no. 4, pp. 86-95, Apr. 2002.
[21] A. F. Naguib, N. Seshadri, A. R. Calderbank, ”Increasing data rate over wireless channels,” IEEE Signal Processing Magazine, vol. 17, no. 3, pp. 76-92, May 2000.
[22] A. N. Barreto, “Antenna transmit diversity for wireless OFDM systems,” IEEE VTC 2002-Spring, vol. 2, pp. 757-761, May 2002.
[23] A. J. Paulraj and C. B. Papadias, “Space-time processing for wireless communications,” IEEE Signal Processing Magazine, vol. 14, pp. 49-83, Nov.
1997.
[24] Helmut Bölcskei, A. J. Paulraj, et al, “Fixed broadband wireless access: state of the art, challenges, and future directions,” IEEE Communication Magazine, vol. 39, no.
1, pp. 100-108, Jan. 2001.
[25] D. Dardari, “Ordered Subcarrier Selection Algorithm for OFDM-Based High-Speed WLANs,” IEEE Trans. Wireless Commu., vol. 3, no. 5, pp. 1452-1458, Sep. 2004.
[26] Q. Liu, S. Zhou, G. B. Giannakis, “Cross-Layer Combining of Adaptive Modulation and Coding with Truncated ARQ over Wireless Links,” IEEE Trans.
Wireless Commun., vol. 3, pp. 1746–1755, Sept. 2004.
[27] J. Campello De Souza, “Discrete bit loading for multicarrier modulation systems,”
PhD. Dissertation, Stanford University, 1999.
[28] V. Erceg, L. Schumacher, P. Kyristsi, “IEEE 802.11 Wireless LANs TGn Channel Models,” IEEE 802.11-03/940r4, May, 2004.
[29] B. Bangerter, E. Jacobsen, M. Ho, A. Stephens, “High Throughput Wireless LAN Air Interface,” Intel Technology Journal, vol. 7, Aug., 2003.
[30] P. Lettieri, M. B. Srivastava, “Adaptive Frame Length Control for Improving Wireless Link Throughput, Range, and Energy Efficiency,” IEEE, 1998.
[31] J. Yin, X. Wang and D. P. Agrawal, “Optimal Packet Size in Error-prone Channel for IEEE 802.11 Distributed Coordination Function,” Wireless Communications and Networking Conference, vol.3, pp.1654–1659, March, 2004.
[32] M. van der Schaar, S. Krishnamachari, Sunghyun Choi, Xiaofeng Xu, “Adaptive cross-layer protection strategies for robust scalable video transmission over 802.11 WLANs,” IEEE Journal on Selected Areas in Communications, vol. 21, NO.10, pp. 1752-1763, Dec. 2003.
[33] V. Erceg, P. Soma, D. S. Baum, and A. J. Paulraj, “Capacity obtained from multiple-input multiple-output channel measurements in fixed wireless environments at 2.5 GHz,” Communications, ICC 2002. IEEE International Conference on, vol. 1, pp. 396-400, May 2002.
[34] I. E. Telatar, “Capacity of multi-antenna Gaussian channels,” European Transactions on Communications, vol. 10, no. 6, pp. 585-595, 1999.
[35] G. G. Raleigh and J. M. Cioffi, “Spatial-temporal coding for wireless communications,” Proc. IEEE 1996 Global Communications Conference, pp.
1809-1814, Nov. 1996.
[36] A. Alexiou, M. Haardt, “Smart Antenna Technologies for Future Wireless Systems:
Trends and Challenges,” IEEE Commun. Magazine, 2004.
[37] F. R. Farrokhi, G. J. Foschini, A. Lozano, and R. A. Valenzuela, “Link-optimal space-time processing with multiple transmit and receive antennas,” IEEE Commun. Letters, vol. 5, no. 3, March 2001.
[38] F. R. Farrokhi, G. J. Foschini, A. Lozano, and R. A. Valenzuela, “Link-optimal BLAST processing with multiple-access interference,” in VTC’2000, Boston, MA, 2000.
[39] X. Li, H. Huang, G. J. Foschini, and R. A. Valenzuela, “Effects of iterative detection and decoding on the performance of BLAST,” IEEE GLOBECOM, vol. 2, pp. 1061-1066, 2000.
[40] E. Biglieri, G. Taricco and A. Tulino, “Decoding space-time codes with BLAST architectures,” IEEE Trans. Signal Processing, vol. 50, no. 10, pp. 2547-2552, Oct.
2002.
[41] Siavash M. Alamouti, “A simple transmitter diversity technique for wireless communications,” IEEE J. Select. Areas Commun., vol. 16, no. 8, pp. 1451-1458, Oct. 1998.
[42] V. Tarokh, N. Seshadri, and A. R. Calderbank, “Space-time codes for high data rate wireless communication: performance analysis and code construction,” IEEE Trans. Inform. Theory, vol. 44, no. 2, pp. 744-765, Mar. 1998.
[43] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space-time block codes from orthogonal designs,” IEEE Trans. Inform. Theory, vol. 45, no. 5, pp. 1456-1467, July 1999.
[44] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space-time block coding for wireless communications: performance results,” IEEE J. Select. Areas Commun., vol. 17, no. 3, pp. 451-460, Mar. 1999.
[45] D. Gesbert, H. Bölcskei, D. A. Gore, and A. Paulraj, “Outdoor MIMO Wireless Channels: Models and Performance Prediction,” IEEE Trans. on Comm., vol. 50,
no. 12, Dece. 2002.
[46] H. Zhang, “The capacity and error probability analysis for high data rate IEEE 802.11 handbook: A designer’s companion,” New York: IEEE Press, 1999.
[47] S. Sandhu and A. Paulraj, “Space-time block codes: A capacity perspective,” IEEE Commun. Letters. Vol. 4, no. 12, pp.384-386, Dec, 2000.
[48] Spatial Channel Model AHG, “Spatial channel model text description,” Tech. Rep.
(File No: SCM-095-SCM Text v2.1b), 3GPP & 3GPP2, Jan. 2003.
[49] P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela, “V-BLAST:
an 1rchitecture for realizing very high data rates over the rich-scattering wireless channel,” URSI International Symposium, pp. 295-300, 29 Sep. -2 Oct. 1998.
[50] L. Schumacher “WLAN MIMO Channel Matlab program,” download information:
http://www.info.fundp.ac.be/~lsc/Research/IEEE_80211_HTSG_CMSC/distributio n_terms.html