Symphony: Orchestrating Collisions in Enterprise Wireless Networks Tarun Bansal (Co-Primary Author), Bo Chen (Co-Primary Author), Prasun Sinha and Kannan Srinivasan Department of Computer Science and Engineering Ohio State University Columbus, Ohio

Enterprise Wireless LAN AP Internet 2

Do we Care for Uplink Uplink traffic is increasing at a rapid pace because: 3 Cloud Computing Online Gaming Sensor Data Upload Code Offloading VoIP, Video Chat

Uplink Traffic: Challenges 4 Traditionally, uplink traffic has received less attention in the design of algorithms/solutions for WLANs Challenging to improve uplink throughput – Single antenna transmitters – Unlike downlink, no global information about which transmitters have packets to send

An Example TDMA (Time Division Multiple Access) with global planning 5 AP 1 AP 2 Alice Bob 2 slots. Switch How many slots does optimal TDMA take?

Same Example with Symphony 6 AP 1 AP 2 Alice Bob Switch AP1 decodes Alice’s packet Subtract Alice’s recreated samples -= Decodes remaining samples to obtain Bob’s packet Two packets received in single slot: Better than optimal TDMA – Requires APs to only exchange decoded packets (and not samples) – Exchanging samples requires prohibitive bandwidth [Gollakota et al. 2009] The two APs act as two different interfaces to the same AP. Optimal TDMA: 2 slots

Example with multiple transmitters 7 AP 1 AP 2 Alice Bob (not in range of AP 1 ) Switch AP1 suppresses Alice Carol (not in range of AP 1 ) Don Symphony Time Slot 1Symphony Time Slot 2: Only Carol and Don transmit AP2 suppresses Bob Optimal TDMA takes 4 slots with one packet transmitted in each slotWhat is the minimum number of time slots required by TDMA? Optimal TDMA: 4 slots - = - = - = Four packets received in two slots No global information about which transmitters have packets to send

8 Challenge: Transmitter Identification C Time Slot 1 at AP 2 A B D SINR of all packets is quite low Time Correlation Value Peak indicates presence of PN sequence [Magistretti et al. 2012] PN Sequence unique to the transmitter [Magistretti et al. 2012] Identify as many transmitters as possible without knowing what they transmitted

Challenge: Computing Set of Transmitters to be Suppressed At the end of each slot, which AP suppresses which transmitter? APs need to make a joint decision – One possible solution: AP 1 suppresses A while AP 2 suppresses D APs need to decode A and D based on samples received in this slot Requires APs to exchange samples: Not a valid solution 9 A A B B 1 AP 1 AP 2 C C D D Linear combination of A and D

Dependence Graph 10 A→ AP 1 One vertex for every link A A B B 1 AP 1 AP 2 C C D D A→ AP 2 B→ AP 2 C→ AP 2 D→ AP 2 D→ AP 1

Dependence Graph: Adding Edges 11 Consider two pairs of links (A → AP 1 ) and (B → AP 2 ) AP 2 cannot decode B until interference from A is cancelled Decoding of A at AP 1 must happen before decoding of B at AP 2 Draw a directed edge from (A → AP 1 ) to (B → AP 2 ) A A B B 1 AP 1 AP 2 C C D D A→ AP 1 A→ AP 2 B→ AP 2 C→ AP 2 D→ AP 2 D→ AP 1 A A B B 1 AP 1 AP 2

Using dependence graph to determine suppressed transmitters Phase 1: Weight (P i → AP j ) = Number of incoming edges X Number of outgoing edges – Weight function improves decoding probability in future – Weight function minimizes the overhead on the backbone 12

Using dependence graph to determine suppressed transmitters 13 D →AP 2 A → AP 1 AP 1 can decode A only after AP 2 has decoded D. AP 2 can decode D only after AP 1 has decoded A Cyclic dependence prevents decoding A A B B 1 AP 1 AP 2 C C D D D →AP 2 A → AP 1

Phase 2 of the algorithm Phase 2: Find the maximum weight induced acyclic subgraph of the dependence graph using a greedy algorithm – Vertices of the acyclic subgraph indicate which APs should suppress which transmitter – Edges of the acyclic subgraph indicate how the APs should exchange packets on the backbone 14 A A B B 1 AP 1 AP 2 C C D D A→ AP 1 B→ AP 2 AP 1 suppresses A AP 1 decodes A and sends it to AP 2 AP 2 suppresses B

Other Challenges in Large Scale Deployment Enterprise WLANs can consist of hundreds of APs What happens when Symphony is deployed to a large scale EWLAN 15

Challenge: No central server No central server – How to compute the dependence graph and the acyclic subgraph? 16 A A B B 1 AP 1 AP 2 C C D D

Challenge: Unreliable Backbone Unreliable backbone with unpredictable latency – When exchanging information among APs, slower link may create bottleneck – Packets may get lost 17

Challenge: Reliability 18 AP 1 AP 2 AP 3 A B C B→ AP 2 A→ AP 1 C→ AP 3 – Long chain of dependence – Decoding failure on one packet leads to failure at all dependent locations A B C

Challenge: Varying density of clients 19 AP 1 AP 2 AP 3 A1A1 B One heavy loaded AP blocks the transmission for the entire network C A2A2 A k-1 AkAk After one time slot…

Experiment Setup USRP Nodes (from Ettus Research): 1078 MHz; BPSK Protocols studied : – Symphony: Distributed Implementation – Flex-Omniscient TDMA Flex (like Symphony): Client can send data to any AP Omniscient: With a central controller that has global queue information apriori – Backbone latency is zero – IEEE (No RTS/CTS) 20

Experiments Setup Topology Clients placed randomly in the three regions Experiments done over multiple topologies 21

Experiment Results RTS OFF Flex-Omniscient TDMA Symphony 4.5x 1.6x

Trace-driven Simulations Setup – SNR data collected from a multiple client-AP testbed (Stanford) Traces used from the experiments – Variation in PN sequence detection accuracy with SINR – Variation in cancellation accuracy with SINR – Variation in backbone delays with number of hops Size of the packets and packet generation times – Generated using SIGCOMM [Schulman et al. 2008] dataset 23

Simulation Results: Throughput 24 On an average, total throughput in Symphony is 1.63x of TDMA 5.6x of RTS OFF (15.1 Mbps) Flex-Omniscient TDMA (51.7 Mbps) Symphony (84 Mbps)

Simulations: Fairness 25 Symphony has higher fairness since it allows all clients to transmit RTS OFF (0.39) Flex-Omniscient TDMA (0.40) Symphony (0.68)

Related Work Cooperative decoding of uplink packets: Bit-level combining [Miu et al. Mobicom 2005], Symbol level combining [Woo et al. Mobicom 2007], Coarse Symbol Representation [Gowda et al. Infocom 2013] – Symphony decodes multiple packets received by multiple APs Exchanging decoded packets over backbone: Interference Alignment [Gollakota et al. Sigcomm 2009] – Requires multiple antennas at both APs and clients MIMO: MegaMIMO for downlink [Rahul et al. Sigcomm 2012] – Symphony focuses on uplink 26

Summary Symphony leverages the unused wired backbone resources to improve the wireless throughput for single antenna systems Symphony design takes into account the challenges that arise in practical large-scale deployments 27