1. CS Colloquium Research Projects in Wireless Communication Networks Xin Liu Computer Sciences Department University of California, Davis

2. 2 Wireless Networks Cellular systems 1G: analog 2G: digital 3G: data Wireless LAN IEEE 802.11 Ad-hoc wireless networks Military, emergency, etc. Wireless Sensor networks

3. 3 Research Topics Digital signal processing Smart antenna Scheduling Power management Topology management Mobility management Routing (for ad hoc networks) ……

4. 4 Unique Features Motivated by some unique features in wireless communication systems : Scarce radio resource Limited power Timing-varying channel conditions Shared media

5. 5 Scarce Radio Resource Wireline networks High bandwidth and reliable channel Core router: Gbps-Tbps Wireless systems Limited nature resource (radio frequency) Capacity is limited by available frequency 3G data rate: up to 2Mbps IEEE 802.11b: up to 11Mbps Requirement: spectrum efficiency

6. 6 Power Battery power is still the bottleneck Important for hand-held equipment Critical for wireless sensor networks What can we do? Power management --- use the available power efficiently

7. 7 Channel Conditions Decides transmission performance Determined by Strength of desired signal Noise level Interference from other transmissions Background noise Time-varying and location-dependent.

8. 8 Interference and Noise

9. 9 Propagation Environment

10. 10 Time-varying Channel Conditions Due to users’ mobility and variability in the propagation environment, both desired signal and interference are time-varying and location-dependent A measure of channel quality : SINR ( Signal to Interference plus Noise Ratio )

11. 11 Illustration of Channel Conditions Based on Lee’s path loss model, log-normal shadowing, and Raleigh fading

12. 12 Performance vs. Channel Condition Voice users: better voice quality at high SINR for a fixed transmission rate; Data users: higher transmission rate at high SINR for a given bit error rate; Adaptation techniques are specified in 3G standards. TDMA: adaptive coding and modulation CDMA: variable spreading and coding

13. 13 Shared Media Shared media: everyone can hear each other Can hurt Can help Multi-user diversity

14. 14 Interference

15. 15 Helper Relay: Coherent Relay:

16. 16 Multi-user Diversity Different users see different channels at different time

17. 17 Opportunistic scheduling Motivation: Spectrum efficiency Time-varying channel conditions Multi-user diversity Question: how to handle channel variability?

18. 18 Opportunism Traditional design: point to point Channel variability: source of unreliability Opportunism: embrace channel variability Multiple users share resource Exploits favorable channel conditions.

19. 19 Myopic Opportunism Greedy algorithm: best user to transmit Good throughput Unfairness Starvation

20. 20 Opportunistic Scheduling Basic idea: schedule users in a way that exploits variability in channel conditions. Opportunistic: choose a user to transmit when its channel condition is good. Fairness/QoS requirements: opportunism cannot be too greedy. Each scheduling decision depends on channel conditions fairness or QoS requirements.

21. 21 System Model Time-slotted systems Each user has a certain requirement. TDMA or time-slotted CDMA systems (e.g., IS- 856, known as Qualcomm HDR) Both uplink and downlink.

22. 22 Overview

23. 23 Performance Measure Based on utility value Reflects channel condition. U i k : utility value of user i at time k. If time slot k is assigned to user i, user i will receive a utility value of U i k. Measures the worth of the time slot to user i. Examples of utility: Throughput Throughput – cost of power consumption. Utility values are comparable and additive.

24. 24 Utility Values { U i k, k=1,2,3… } is a stochastic process.

25. 25 A Framework for Opportunistic Scheduling Objective: Maximize the sum of all users’ utility values while satisfying the QoS requirements of users. Scheduling decision depends on: Utility values (reflecting channel conditions) QoS/fairness requirements.

26. 26 A Case Study: Temporal Fairness Scheduling

27. 27 Objective Maximize average system utility subject to the fairness constraints r i. System utility:

28. 28 Scheduling Problem Formulation Optimal scheduling problem where  is the set of all policies. No channel model assumed. No assumption on utility functions. General distributions of. Users’ utility values can be correlated.

29. 29 An Optimal Scheduling Policy Choose the ``relatively-best'' user to transmit. v i * : “off-sets” used to achieve the fairness requirement.

30. 30 Property Improves performance for all. Gain depends on channel variability. A certain level of average utility guarantee for each user.

31. 31 Scheduling Gain Opportunistic scheduling gain increases with channel independence (across users) channel variability (over time) number of users.

32. 32 System Performance

33. 33 Joint Scheduling and Power Allocation Joint scheduling and power allocation: intercell-interference management. Interference limits the system capacity. Power allocation: interference management. Opportunistic scheduling: multi-user diversity. Two decision variables: which user how much power.

34. 34 Objectives Objective 1: minimize total transmission power guarantee a minimum-utility for each user. Objective 2: maximize net utility tradeoff between throughput and transmission power (interference to other cells). guarantee a minimum-utility for each user.

35. 35 A To-do List May induce variability if needed. Can be used in distributed manners. Many to many Large sensor networks Real-time traffic Multi-carrier systems A different design aspect Problems in information theory Future wireless systems: exploit opportunistic methods (IS-856).

36. 36 Wireless Sensor Network Potential Micro-sensors, on- board processing, and wireless interfaces all feasible at very small scale can monitor phenomena “up close” Will enable spatially and temporally dense environmental monitoring will reveal previously unobservable phenomena Seismic Structure response Contaminant Transport Marine Microorganisms Ecosystems, Biocomplexity Ref: based on slides by D. Estrin

37. 37 Enabling Technologies EmbeddedNetworked Sensing Control system w/ Small form factor Untethered nodes Exploit collaborative Sensing, action Tightly coupled to physical world Exploit spatially and temporally dense, in situ, sensing and actuation Ref: based on slides by D. Estrin

38. 38 Challenges By no means this is a complete list: Self-configured Random deployment of sensor networks Long-lived sensor systems Sensors have very limited battery power Reliability Harsh environment Unreliable sensors Cost Scalability Massive data Compression and aggregation Time synchronization, data query, localization, storage, etc.

39. 39 A Random Deployed Sensor Network GATEWAY MAIN SERVER CONTROL CENTER

40. 40 Topology control Many-to-one communication Unbalanced load Uneven power consumption “Important” nodes in the route die quickly Possible approaches More power at closer nodes Data compression and aggregation

41. 41 The Problem Objective: minimize # of sensors needed to build a sensor network that covers a given area for a certain amount of time. Communication consumes a lot of power R: rate, D: distance between transmitter and receiver Put nodes with heavier load closer

42. 42 Approach Non-trivial: sensor placement, routing, power management To consider: Linear and planar network Random and non-random topology Other power consumption Approaches: Understand fundamental principles Build practical solutions P1 P2

43. 43 Coverage and Connectivity

44. 44 Coverage and Connectivity Traditional work: full coverage and connectivity, K-coverage, etc. Our objective: Cover and connect a large portion of the area Quantify the size of uncovered area How many nodes needed What is the density needed

45. 45 Cost and Reliability Layered structure More expensive nodes with more functionality Objective: minimize the total cost, including different types (cost) of nodes, while maintaining the desired performance Reliability important, especially for large scale network nodes damages, out of power, etc.

46. 46 Parking Lot Patrol Problem Sensors on parking meters Build a wireless sensor network to report illegal parking Patrolman to find the reported events Applications: Border patrol Speeding monitoring

47. 47 What Do We Stand? History: a successful story, an industry of $$$$$$ Current: Policy re-examination underway Increased unlicensed spectrum allocation Exploration of “underlays”, e.g., UWB Exploration of “overlays”, e.g., opportunistic use of committed but unused bandwidth Future: more spectrum better ratio equipment, DSP technologies, longer battery life Better networks Cool applications

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