Why PHY Really Matters Hari Balakrishnan MIT CSAIL August 2007 Joint work with Kyle Jamieson and Ramki Gummadi

MIT 6.02 (Introduction to EECS 2)  EECS via a lab-based cross-cutting course on communication systems  “From Fourier transforms to the Internet”

Achieving High Throughput  Must increase parallelism (concurrency)  Must adapt over multiple time scales  Must adapt to spatial variations in demand  PHY cooperation crucial for higher layers to do these tasks well

Traditional Approach  Provision the network in advance  Generally for full coverage, assuming some number of users in each area (cell)  Allocate spectrum assuming worst case in each cell  Usually (e.g., in buildings), leads to frequency division  Then, increase spatial reuse using collision avoidance  Design PHY links to ensure low BER (say, )  Adapt channel coding, modulation by watching what happens over channel  Adapt route by watching what happens over path (but without PHY input)

Inefficiency 1: Static Provisioning is Pessimistic

Inefficiency 2: Collision Loss  Lots of packets lost to collisions and noise in wireless networks Non-colliding bits (P1) (P2) Time Can’t recover non-colliding bits today!

What We Want  Wireless networks should “spread spectrum on demand”  Track temporal and spatial variations  Adapt to the actual case, not the assumed or worst case  Intertwine resource provisioning and allocation  Receivers should be able to recover partial packets  Increases concurrency (more aggressive MACs possible)  Increases tolerance to noise  PHY links can then be designed for much higher BER (and so much higher data rates)  BER of rather than  Will need new techniques for ARQ, forwarding, channel access, rate adaptation, etc.

On-Demand Wireless Example  Take entire band (e.g., 83.5 MHz for ) and allocate dynamically  Assume each node can communicate using entire band  Use orthogonal CDMA codes aggressively  Assume M >> 1 codes  Each node i gets c i codes proportional to demand  Can develop random code allocation schemes  Need to avoid reuse in same spatial area  Many other schemes possible

Recovering Partial Packets 1.How does receiver know which bits are correct? 2.How does receiver know P2 is there at all? 3.How to use partial packets in ARQ and forwarding protocols? Two classes of schemes: where nodes collaborate (multi-radio diversity) and where nodes don’t

SoftPHY High uncertainty PHY conveys uncertainty in each bit it delivers up Low uncertainty SoftPHY implementation PHY-dependent, but interface should be PHY-independent Result: On DSSS/MSK (Zigbee), we improve by x [sigcomm07] Open question: optimal SoftPHY implementation for different PHYs

SoftPHY for Spread Spectrum  Demodulate MSK chips  Decide on closest codeword to received (Hamming distance)  Many 32-bit chip sequences are not valid codewords  Codewords separated by at least 11 in Hamming distance  similar Transmitter: Receiver: SoftPHY hint: Hamming distance between received codework and decided-upon codework

Conclusion (Open Questions)  SoftPHY has potential for significant throughput gains  Need advances at PHY  Developing best schemes for each PHY  E.g., Matched filter output, decoder output, …  Need advances in network algorithms to use SoftPHY  Suppose each bit k correct with probability p k  Efficient ARQ design  Efficient forwarding protocol  Efficient channel access (some collisions OK)  Need cross-layer methods to handle spatial and temporal variations