Research Projects in Wireless Communication Networks

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

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

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

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. 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. 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. Interference and Noise

9. Propagation Environment

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

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

14. Interference

15. Relay: Helper Coherent Relay:

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

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

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

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. 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. Overview

23. Performance Measure Based on utility value Reflects channel condition. Uik: utility value of user iat time k . If time slot k is assigned to user i, useri will receive a utility value of Uik. 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. Utility Values • {Uik, k=1,2,3…} is a stochastic process.

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. A Case Study: Temporal Fairness Scheduling

27. Objective • Maximize average system utility subject to the fairness constraints ri. • System utility:

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

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

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

32. System Performance

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. 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. 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. 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 Ecosystems, Biocomplexity Marine Microorganisms Ref: based on slides by D. Estrin

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

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. A Random Deployed Sensor Network GATEWAY MAIN SERVER CONTROL CENTER

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. 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. 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. Coverage and Connectivity

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. 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. 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. 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 An exciting area, a long way to go!

48. Thank You!