MIMO Layered Multiuser Detection in Ad Hoc Networks

1. MIMO Layered Multiuser Detection in Ad Hoc Networks Dr. Michele Zorzi, zorzi@cts.ucsd.edu University of California at S.Diego M. Zorzi’s work was supported under the MURI Work in collaboration with P. Casari and M. Levorato, Ph.D. students at the University of Padova, Italy

2. Preamble: Spatial Multiplexingin point-to-point links • First introduced by Foschini [1], to enhance the raw available PHY bit rate in a point-to-point link by spatial superposition of multiple bit streams • A TX terminal with A antennas sends a different stream over each antenna, and the RX terminal uses its own antennas to spatially separate and decode the incoming streams. Bit rate is A times higher • Alternative approach: multiple users, with multiused detection and interference cancellation at the RX [2] [1] G. J. Foschini, “Layered space–time architecture for wireless communication in a fading environment when using multi-element antennas,” Bell Labs Tech. J., vol. 1, pp. 41–59, 1996. [2] S. Sfar, R. D. Murch, and K. B. Letaief, “Layered space–time multiuser detection over wireless uplink systems,” IEEE Trans. Wireless Commun., vol. 2, no. 4, pp. 653–668, July 2003.

3. … LAST-MUD Algorithm - 1 • Let each of the K users transmit with one antenna • Each user sends its own transmission to the RX • The RX sees a superposition of signals at each RX antenna and wants to separate them. Received signal: • Za is the channel vector, b the symbols, na the noise Aantennas K users

4. LAST-MUD Algorithm - 2 • The decision statistics at the receiver is • I is the space-filtered interference • is the space cross correlation matrix • n is the filtered Gaussian noise vector • Note: interfering signals in ra are jointly detected, those in I are unkown interference

5. LAST-MUD Algorithm - 3 • Subsequent decoding steps: • Calculate R’s pseudoinverse, i.e., • Re-order received symbols according to post-detection SNR • Extract the index of the symbol with highest SNR • Weigh the sufficient statistics vector components withthe -th column of to yield a scalar value • Feed the scalar value into the decision block and estimate the corresponding symbol, • Update vector M by erasure of symbol ’s contribution • Update R by striking out the -th row and column • Step to next symbol detection ( )

6. … … … … Ad hoc network scenario Aantennas Used TX antennas • Each wireless terminal has A antennas available • It uses a subset of these antennas to transmit (e.g.,2 transmits to 1 with 4 antennas, while K only uses 2) • A RX terminal always uses all available antennas for detection purposes Aantennas Aantennas Used TX antennas Aantennas Terminal 2 Terminal K … Terminal 1

7. LAST-MUD in ad hoc networks • TX terminals may use more than one antenna • Depends on data to send and channel/interf. conditions • each antenna output is seen as a different “user” by PHY • Goal: rule radio access (schedule and rate) in order to exploit layered multiuser detection benefits • Approach: • Make simulations of PHY behavior in an ad hoc network scenario to understand pros and cons of the technique • Based on these simulations, provide design guidelines for a MAC layer protocol that exploits LAST-MUD • Before TX, decide how much data to send and to whom • During RX, decide which signals go into the interference cancellation term and which are unknown interference

8. Example of PHY results: high load • Here a terminal with 8 RX antennas listens to 4 users transmitting 4 streams each for a total of 16 streams • At 50 m, the prob of correct decoding of all is very high: • High SNR results in good spatial separation of the wireless channels • At larger distances, performance degrades because of the reduced SNR and resulting imperfect interference cancellation P[no. of bit errors > abscissa]

9. PHY results: more on high load • The ability to receive many streams at short distance is very important • In an ad hoc network, nodes will then be able to: • Decode many spatially superimposed data packets, if they come from near neighbors • Decode many signaling messages (RTSs, CTSs, ACKs) as they are shorter and easier to decode • Gain information about (local) network load and make scheduling, access, and rate decisions P[no. of bit errors > abscissa] See: Paolo Casari, Marco Levorato, Michele Zorzi, “Some issues concerning MAC Design in Ad Hoc Networks with MIMO communications,” WPMC 2005, Sep. 2005.

10. PHY results: some conclusions • In an ad hoc network scenario: • Signaling packets (transmitted with a single antenna) can be received without errors at significant distances • Data packets (that require spatial multiplexing) may be decoded in high numbers only if coming from short distances • Interference from unestimated streams is an issue, so that nodes should try to gain knowledge of traffic conditions in their neighborhood and decide what to decode and cancel and what to ignore • Effect/benefits of channel coding still under study • Some first results in the WPMC paper, more accurate characterization under way

11. Simulation of an ad hoc network:main assumptions • MATLAB simulator for LAST-MUD in ad hoc nets • Main assumptions: • Collision avoidance mechanism based on RTS and CTS transmission; correct DATA reception is confirmed by ACK • Completely connected, grid-topology network, nodes within carrier sensing range of each other (a sort of “worst case”). • Transmissions are in frames: all RTSs are transmitted simultaneously in a slot, and so are CTS, DATA and ACK • Receivers track channels for the incoming signals (for MUD) • Only a limited number (32 in our results – need to decide which ones!), the others will be unknown interference RTS CTS DATA ACK …… RTS CTS DATA ACK

12. Simulation of an ad hoc network: MAC layer operations • Each node keeps a queue of unsent packets • RTSs are sent to request permission to TX packets • Antennas allocated according to length of packet (1000 bits per antenna per transmission). RTS contains this info. • Receivers can estimate the traffic in the upcoming frame, thanks to the ability to receive multiple (all?) RTSs • CTSs are issued based on the received RTSs • Based on traffic and interference/channel estimates, potential receivers decide how many transmissions (and which ones) to allow • MUD – Interference cancellation • Each receiver allocates its degrees of freedom to receive intended data and track and cancel (some of) the interferers • How this is done is important for good performance!

13. Simulation of an ad hoc network: MUD policies • NFT (do Not Follow Traffic) • The node only tracks the streams directed to itself and neither decodes nor cancels other streams; all transmissions meant for other nodes are part of the unknown interference term • PFT (Partially Follow Traffic) • Each node first uses its degrees of freedom to track the channels of all incomingstreams, and then uses the remaining resources (if any) to detect and cancel interference coming from other streams; • FT (Follow Traffic) • Each node tracks the channel of the strongest signals (with the obvious constraint that at least one must be meant for it), thereby providing maximum interference capabilities and greatly improving the reception performance (traded off for throughput) • Note: in PFT and FT, decisions on which signals to receive translate into how CTSs are issued • Cross layer approach: • MAC makes access decisions based on PHY conditions (e.g. power) • How PHY detects signals is directed by the MAC decisions

14. MAC results: throughput • PFT and FT perform better (as expected), as they estimate and cancel interference as well • NFT very poor • FT has the best performance, as it balances • The need to decode useful pkts • The need to protect them by canceling unwanted interference • FT’s max throughput is 8 pkts/slot • As many as there are RX antennas, and the network is fully connected: good result • Need to explore this further for better understanding

15. More results • Other results obtained: • Packet success ratio (percentage of the attempted transmissions that actually get through) • Queue length (how packets get backlogged) • Protocol efficiency (percentage of the throughput that actually corresponds to useful traffic – accounts for protocol overhead) • In all cases, the FT scheme is seen to be clearly superior • Not shown here for lack of time, please see: • Paolo Casari, Marco Levorato, Michele Zorzi, “On the implications of layered space-time multiuser detection on the design of MAC protocols for ad hoc networks,” IEEE PIMRC 2005, Sep. 2005.

16. Conclusions • Layered multiuser detection is a very promising technique to be deployed in ad hoc networks with multiple antennas • The use of LAST-MUD paves the way for effective cross-layer design of MAC protocols, which in turn leads to better protocol performance • Protocol enhancements may be obtained only if a correct knowledge of the physical layer behavior is properly taken into account

17. Future work • Carry out a more detailed performance study • Study the effect of coding and other PHY issues (waveform?) • Work on PHY approx. to avoid bit-level simulations • Include multihop operation in the evaluation • Consider other (better) MAC policies • We do not claim FT and PFT are optimal • Study the broadcast problem with MIMO/directional antennas • Study the effect of asynchronous packet transmission

18. Backup slides follow

19. MAC results: success ratio • Here, the average ratio of successfully decoded streams to sent streams is depicted • As before, NFT has the worst perf., and FT with exp backoff has the best one • This is because FT with exp backoff is a good coupling, that both achieves good decoding performance (FT) and still solves traffic congestion (exp backoff)

20. MAC results: queue length • Here the average node queue length is depicted • As before, FT is best, in the sense that its ability to manage traffic and decoding procedures lets packets be handled effectively if traffic is not too high

21. MAC results: protocol efficiency • Here the average protocol efficiency (i.e., the average ratio of correctly decoded data bits to the total sent bits) is depicted. • For the already cited motivations, FT shows the best performance • Better decoding perf. and better traffic handling means less wasted data streams and higher efficiency