Hop Optimization and Relay Node Selection in Multi-hop Wireless Ad-hoc Networks

1. Hop Optimization and Relay Node Selection in Multi-hop Wireless Ad-hoc Networks Xiaohua (Edward) Li Department of Electrical and Computer Engineering State University of New York at Binghamton xli@binghamton.edu

2. 1.Introduction • Multi-hop transmission • Benefits: • Make network flexible, • Create larger network • Save energy of source node • Deficits: • Increase system complexity • Extra interference among relay nodes

3. 1.Introduction(cont' ) • Our objective • Analyze mutual interference & cooperation • Derive node SINR • Develop efficient & scalable ways to optimize path capacity

4. 2.System Model • Consider H-hop relaying in wireless network with J+1 nodes • Each node has transmission power p • Node i receives signal power pdij-αfrom node j dij: distance between node i and j α： path-loss exponent

5. 2.System Model: slotted transmission protocol

6. 2.System Model: received signal of node i dij: distance between node i and j α： path-lossexponent u(k): signal at time k vj(k):noise N: power of noise

7. 3. SINR Analysis and Optimization Signal known by Node j Simplify the received signal

8. 3. SINR Analysis and Optimization (cont’) • SINR of node j at time k • SINR of node j at time k-1 Notice: here we track the same packet u(k-j+1)

9. 3. SINR Analysis and Optimization (cont’) • For the detection of u(k-j+1) • Apply maximal ratio combing (MRC) to find the maximum SINR of yj(k)

10. 3. SINR Analysis and Optimization (cont’) • Calculate the overall SINR with MRC Notice: This SINR is for a node j in an H-hop when detecting packets

11. 3. SINR Analysis and Optimization (cont’) • Calculate path capacity Like a water pipe, the capacity is limited by the minimum tunnel • Network-wide optimization for optimal relaying path

12. 4. Hop optimization and node selection • First term of sj is the dominating one (when l=0) • Formulate the problem to a max-min scheme

13. 4. Hop optimization and node selection (cont’) • With some approximation, the complex min-max optimization can be simplified to a simple equation solving: • Find d0 from • Find other dj from:

14. 4. Hop optimization and node selection (cont’) • Apply previous method and locate the points • M nodes are closed to these points • Select nodes closest to the calculated points

15. 5. Simulations

16. 5. Simulations (cont’)

17. 6. Conclusion • An efficiency way to optimize multi-hop wireless networks in hop node selection • Efficient and scalable for large networks • Consider node cooperation and competition