Tài liệu Distributed MIMO - Patrick Maechler docx

Distributed MIMO Patrick Maechler April 2, 2008 Outline 1. Motivation: Collaboration scheme achieving optimal capacity scaling 2. Distributed MIMO 3. Synchronization errors 4. Implementation 5. Conclusion/Outlook Throughput Scaling ● Scenario: Dense network – Fixed area with n randomly distributed nodes – Each node communicates with random destination node at rate R(n). Total throughput T(n) = nR(n) ● TDMA/FDMA/CDMA: T(n) = O(1) ● Multi-hop: T(n) = O( ) – P. Gupta and P. R. Kumar, “The capacity of wireless networks,” IEEE Trans. Inf. Theory, vol. 42, no. 2, pp. 388–404, Mar. 2000. ● Hierarchical Cooperation: T(n) = O(n) – Ayfer Özgür, Olivier Lévêque and David N. C. Tse, ”Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks”, IEEE Trans. Inf. Theory, vol. 53, no. 10, pp. 3549-3572, Oct. 2007 n Cooperation Scheme ● All nodes are divided into clusters of equal size ● Phase 1: Information distribution – Each node splits its bits among all nodes in its cluster Cooperation Scheme ● Phase 2: Distributed MIMO transmissions – All bits from source s to destination d are sent simultaneously by all nodes in the cluster of the source node s Cooperation Scheme ● Phase 3: Cooperative decoding – The received signal in all nodes of the destination cluster is quantized and transmitted to destination d. – Node d performs MIMO decoding. Hierarchical Cooperation ● The more hierarchical levels of this scheme are applied, the nearer one can get to a troughput linear in n. Outline 1. Motivation: Collaboration scheme achieving optimal capacity scaling 2. Distributed MIMO 3. Synchronization errors 4. Implementation 5. Conclusion/Outlook Distributed MIMO ● Independent nodes collaborate to operate as distributed multiple-input multiple-output system ● Simple examples: – Receive MRC (1xN r ): – Transmit MRC (N t x1, channel knowledge at transmitter) – Alamouti (2xN r ): STBC over 2 timeslots ● Diversity gain but no multiplexing gain wxhy h h xnxhy         +==+= |||| |||| ˆ , * Alamouti, S.M., "A simple transmit diversity technique for wireless communications ," Selected Areas in Communications, IEEE Journal on , vol.16, no.8, pp.1451-1458, Oct 1998 MIMO Schemes ● Schemes providing multiplexing gain: – V-BLAST: Independent stream over each antenna – D-BLAST: Coding across antennas gives outage optimality (higher receiver complexity) nxHy  += [1] P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela. V-BLAST: An architecture for realizing very high data rates over the rich scattering wireless channel. In ISSSE International Symposium on Signals, Systems, and Electronics, pages 295-300, Sept. 1998. [2] G. Foschini. Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell Labs Technical Journal, 1(2):41-59, 1996. [...]... Frequency-selectivity ● ● ● Synchronization errors make flat channels appear as frequency-selective channels Receivers for freq.-sel channels can perfectly compensate synchronization errors Implementation cost is much higher! Time Shift - SIC ● Promising results for SIC receiver that samples each stream at the optimal point – Compensation of synchronization errors possible for independent streams (V-BLAST)... scheme achieving optimal capacity scaling 2 Distributed MIMO 3 Synchronization errors 4 Implementation 5 Conclusion/Outlook Conclusion/Outlook ● ● Standard flat-channel MIMO decoders useable for synchronization errors up to 20% of symbol duration More complex decoders can compensate different delays also for higher errors Outlook: ● BEE2 implementation of MIMO receiver ● Frequency synchronization methods... synchronization errors possible for independent streams (V-BLAST) Outline 1 Motivation: Collaboration scheme achieving optimal capacity scaling 2 Distributed MIMO 3 Synchronization errors 4 Implementation 5 Conclusion/Outlook Implementation ● ● Goal: Show feasibility of distributed MIMO Systems using BEE2 boards Focus on synchronization algorithms at receiver – – Frequency synchronization – ● Timing synchronization... interference cancelation (SIC) Er r or Rate Comparison ● MMSE-SIC is the best linear receiver ● ML receiver is optimal Outline 1 Motivation: Collaboration scheme achieving optimal capacity scaling 2 Distributed MIMO 3 Synchronization errors 4 Implementation 5 Conclusion/Outlook Synchronization ● Each transmit node has its own clock and a different propagation delay to destination – No perfect synchronization.. .MIMO Decoders ● Maximum likelihood: ˆ xML = arg min x∈χ (| y − Hx |) ● Zero Forcing / Decorrelator ● ˆ xZF = H + y, H + = ( H * H ) −1 H * 1   =  H *H + I  H*y SNR   MMSE – ● −1 ˆ xMMSE Balances noise and multi stream interference (MSI) Successive interference cancelation (SIC) Er r or Rate Comparison ● MMSE-SIC is the best linear receiver ● ML receiver . Distributed MIMO Patrick Maechler April 2, 2008 Outline 1. Motivation: Collaboration scheme achieving optimal capacity scaling 2. Distributed MIMO 3 scaling 2. Distributed MIMO 3. Synchronization errors 4. Implementation 5. Conclusion/Outlook Distributed MIMO ● Independent nodes collaborate to operate as distributed