Radio Resource Allocation

1. Radio Resource Allocation MURI Kick-Off Meeting June 15, 2004 R. L. Cruz, UCSD Michele Zorzi, Univ. of Padova, Italy

2. Outline of Talk • Fixed wireless multi-hop networks (Cruz) • Joint power control, scheduling, routing, beamforming • Series of papers, joint work w/ Arvind Santhanam, Bongyong Song, Yih-Hao Lin, Prof. Bhaskar Rao • Routing in mobile, ad-hoc wireless networks (Zorzi) • Geographic Random Forwarding (GeRaF) • Joint work with Prof. Ramesh Rao • Discuss contrast between model assumptions • Directions for future research collaborations

3. Radio Resource Allocation in Fixed Wireless Networks R. L. Cruz Dept. of Electrical & Computer Engineering University of California, San Diego Joint work with Arvind Santhanam, Bongyong Song, Yih-Hao Lin, & Bhaskar Rao

4. TARGET APPLICATION AREAS FOR FIXED WIRELESS NETWORKS • Wireless Infrastructure Extension Networks • e.g., each node above is 802.11 Access Point • Focus on Transport of Aggregated Traffic Between Access Points • Residential Broadband Internet Access

5. Example of a Multihop Wireless Network • PROBLEM DEFINITION • Given Average Data Rate Requirement per Link • Given Peak Transmission Power per Node • All Path Gains are Assumed to be Known and Fixed • FIND SCHEDULE OF POWER ALLOCATIONS AND ANTENNA WEIGHTS THAT MEET ABOVE CONSTRAINTS AND MINIMIZE TOTAL AVERAGE POWER

6. MIMO Multi-hop Wireless Networks • Network Configuration • A single node can participate in multiple links as a source and/or a destination • Multiple links from/to a single node can be simultaneously active using the SDM/SDMA principle

7. System Model • Network Configuration • A multi-hop network composed of N nodes and L directional links • Q tx. antennas and P rx. antennas • Array Processing : Beamforming • A set of Tx. array weight vectors • A set of Rx. array weight vectors • Power Control • infinitesimal granularity • Node i is subject to the peak power constraint • Network Power Vector

8. System Model (2) • Transmission Mode : a set • Policy : Transmission Mode Scheduling • Frame format • A policy , • Average Minimum Rate Requirement : • QoS support

9. System Model (3) • Channel Model : Flat fading MIMO channel • Matrix Channel (PQ) • LOS channel with standard ULA (Uniform Linear Array) where • Received signal vector at the receiver of link • Spatial covariance matrices

10. System Model (4) • SINR where : effective link gain • Data Rate Model • monotonically non-decreasing function of SINR • Single Rate / Multiple Rate (link adaptation) • Logarithmic in SINR ( 0.16 ≤ k ≤ 1 ) • Linear in SINR

11. Resource Allocation Example: String Topology (Traffic Relay Network) DestinationNode SourceNode Ratio of Maximum Throughput Achievable with Optimal Scheduling and Power Control to that of TDMA versus ambient noise power:

12. Transmission mode {(1,2),(4,5)} is used here (concurrent transmissions) {(1,2),(3,4)} is also used here TDMA is optimal here Resource Allocation Example: String Topology (Traffic Relay Network) DestinationNode SourceNode Total Power Requirement for Optimal Scheduling and Power Control versus System Throughput (for 3rd data point in previous figure)

13. Resource Allocation Example: Diamond Topology (Optimal Routing Demo) DestinationNode SourceNode Slightly asymmetric: 1--> 2 --> 4 is Minimum Energy Route Ratio of Maximum Throughput Achievable with Optimal Routing, Scheduling and Power Control using Multiple Routes to that when only the Minimum Energy Route is Used, versus Ambient Noise Power

14. Alternate (less efficient) Route 1 --> 3 --> 4 is used: {(1,2),(3,4)} and {(1,3),(2,4)} (ConcurrentTransmissions) TDMA along Minimum Energy Route used here Resource Allocation Example: Diamond Topology (Optimal Routing Demo) DestinationNode SourceNode Slightly asymmetric: 1--> 2 --> 4 is Minimum Energy Route Total Power Requirement for Optimal Routing, Scheduling and Power Control versus System Throughput (for 3rd data point in previous plot)

15. Partition Links into Disjoint Clusters, e.g. 1 - 16 here: Hierarchical Approach for Large Networks • Scheduling in Each Cluster is Performed Independently • Inter-cluster Interference is Modeled by Constant Power Ambient Noise (THIS IS CONSERVATIVE!) • Inter-cluster coordination for distant clusters is weak: Solve Fixed Point Equation

16. Hierarchical Approach for Large Networks • EXAMPLE: 3 Cluster Schedules are considered: • Schedule X: All 16 Clusters on Always • Schedule Y: Checkerboard Alternation between 2 Cluster Groups

17. Hierarchical Approach for Large Networks • EXAMPLE: Three Cluster Schedules are considered: • Schedule X: All 16 Clusters on Always • Schedule Y: Checkerboard Alternation between 2 Cluster Groups • Schedule Z: Alternation between 4 Cluster Groups

18. Hierarchical Approach for Large Networks • Performance Evaluation of Cluster Schedules X,Y,Z • Total Average Power vs. Network Throughput (all links have equal rates) • No Schedule Dominates for All Data Rates • Can Combine Schedules via Time Sharing (Convex Hull)

19. Joint Power Control and Beam-forming Optimization of Single Transmission Mode Given target data rates (SINRs) for each active link in transmission mode Joint optimization of power vector P, transmit weight vector V, receive weight vector U Non-convex optimization problem, currently developing promising heuristics based on network duality

20. Possible Future Directions • Development of integrated heuristics (routing, scheduling, power control, beam-forming) for large scale fixed networks (on-going work) • Consideration of time varying channels (fading) • Consideration of vector channels for each link (MIMO vs beam-forming) • Reaction strategies for external interference (jamming) • Distributed protocols for channel measurement

21. Geographic Random Forwarding (GeRaF) “Geographic Random Forwarding (GeRaF) for Ad Hoc and Sensor Networks: Multihop Performance,” by Michele Zorzi and Ramesh R. Rao, IEEE Transactions on Mobile Computing, Oct-Dec, 2003 “Geographic Random Forwarding (GeRaF) for Ad Hoc and Sensor Networks: Energy and Latency Performance,” by Michele Zorzi and Ramesh R. Rao, IEEE Transactions on Mobile Computing, Oct-Dec, 2003

22. GeRaF basic idea

23. Possible Future Directions Use of antenna arrays tradeoffs between diversity of reception and reliability of each link Integration of protocols optimized for different environments centralized versus distributed uncertainty in channel parameters mobile nodes versus fixed nodes

24. Backup Slides

25. Convex hull: describes feasible average values Universe of possible actions in a single slot. Transmitter is idle Transmitter is on “full blast” Optimal operating point Duality Approach Suppose network consisted of a single link: P1 0 C1 C1-X1

26. Duality Approach Convex hull: describes feasible average values Universe of possible actions in a single slot. Optimal operating point Suppose network consisted of a single link: P1 C1-X1

27. Convex hull Transmitter 1: full blastTransmitter 2: full blast Transmitter 1: full blastTransmitter 2: idle Optimal operating point (avg) Transmitter 1: full blastTransmitter 2: idle Both transmitters idle Duality Approach Suppose network consisted of a two links: P1+P2 C1-X1 C2-X2

28. Resource Allocation Example: Access Network 1 --> 3 --> 6, 2 --> 3 --> 6 and 3 --> 6 are Minimum Energy Routes SourceNodes DestinationNode Ratio of Maximum Throughput Achievable with Optimal Routing, Scheduling and Power Control using Multiple Routes to that when only the Minimum Energy Route is Used, versus Ambient Noise Power