Impact of Channel Heterogeneity on Spectrum Sharing in Cognitive Radio Networks

1. Impact of Channel Heterogeneity on Spectrum Sharing in Cognitive Radio Networks Advisor: Wen-Hsing Kuo Presenter: Che-Wei Chang Miao Ma; Tsang, D.H.K.; Communications, 2008. ICC '08. IEEE International Conference on 19-23 May 2008 Page(s):2377 - 2382 Digital Object Identifier 10.1109/ICC.2008.452

2. Abstract • Previous studies on spectrum sharing focused on the formulations with homogeneous channels. • The channel heterogeneity, which is a unique feature in cognitive radio networks, has been ignored. • They model the : • channel heterogeneity • interference constraints • spectrum sharing • formulate an optimization problem in the form of binary integer linear programming (BILP).

3. Outline • Introduction • Channel Heterogeneity • Assumptions and System Model • Modeling of Spectrum Sharing • Numerical Results • Conclusion

4. Introduction(1/2) • we are interested in the design of an efficient spectrum sharing algorithm among the secondary users which takes into account the interference considerations and channel heterogeneity. • Wang et al. [4] formulated the channel allocation problem among secondary users as a list-coloring problem. • Zheng et al. [5] developed a graph-theoretical model to characterize the spectrum access problem under a number of different optimization functions. • Thoppian et al. [6] presented a formulation of MAC-layer scheduling in CR networks.

5. Introduction(2/2) • Hou et al. [7] modeled the spectrum sharing and sub-band division, scheduling and interference constraints, and flow routing. • Shi et al. [8] developed a formal mathematical model for scheduling feasibility under the influence of power control. • Ma et al. [9] proposed a cross-layer design on spectrum sharing and power control which considers bidirectional links. • we assume the channels are heterogeneous, bi-directional links and adopt an 802.11-style protocol interference model. • homogeneous node location and heterogeneous node location.

6. Channel Heterogeneity • Heterogeneous Transmission Ranges • Heterogeneous Set of Available Channels

7. Heterogeneous Transmission Ranges Cognitive Radio (CR) Multi-channel multi-radio (MCMR) Different channels have a common transmission range Different channels may have different properties

8. Heterogeneous Set of Available Channels In MCMR network The set of available channels at each node is identical. In CR network • Each secondary user individually detects available channels, and the set of available channels that can be used for communication is different from node to node.

9. In cognitive radio, we have secondary users. Each secondary user i (where 1 ≤ i ≤ n) has a programmable number of radio interfaces . Assumptions and System Model • Heterogeneous Channels • Bidirectional Links • Static Node Location with Centralized Server

10. Let denote the set of available channels observed by node and denote the number of available channels. We have , and the cardinality . There are types of channels. The maximum transmission range of each type (denoted by , ) of channels is and the corresponding interference range is . Heterogeneous Channels(1/2) There are orthogonal channels in the network, denoted by the set and the cardinality. is the guard zone.

11. We list the transmission ranges in ascending order, i.e., < < … < . is the set oftype channels shared by node and node . Heterogeneous Channels(2/2) we can divide the channels in the set into subsets according to the channel type

12. We represent the CR network with an undirected graph , where is the set of secondary users and is the set of edges between two secondary users. includes all the possible links. We let and denote the set and the number of available channels for the link , respectively. Bidirectional Links(1/2) Two reasons for bidirectional links: (1) Wireless channels is lossy. (2) For the 802.11 MAC protocol, a RTS-CTS exchange is usually used to perform virtual carrier sensing.

13. Bidirectional Links(2/2) when , can the channel in the set be used by the link. where Obviously we have

14. Static Node Location with Centralized Server • We assume that: • the locations of the secondary users are static. • the set of available channel at each secondary user is static. • there exists a centralized server in CR networks.

15. Modeling of Spectrum Sharing • Link Assignment • Interference Model • Interference, Link and Node Constraints • Problem Formulation Spectrum sharing can be done either in time domain or frequency domain. Spectrum sharing is to determine which link is going to be active and which channel will be assigned to each active link.

16. Link Assignment For direct communication, two secondary users need to be within the transmission range of each other. Recall that we assume the links are bidirectional. We say link e is active only if some channel m has been assigned to link e.

17. The transmission on link e by using channel (where ) is successful if and only if • For any link being assigned the same channel m, the receiving nodes i and j must be out of the interference range. i.e., for Interference Model(1/2) Interference Model(1/2) Interference Model(1/2) • 802.11-Style Protocol Interference Model:

18. Let denote the set of possible links incident on node Let denote the set of links which interfere with link by using channel . Interference Model(2/2)

19. Interference, Link and Node Constraints • Interference Constraint • Link-Channel Constraint • Node-Radio Constraint • Additional Constraint

20. Interference only occurs among the links that share the same channel. if link e is active on channel m, then channel m cannot be assigned to any link as long as. Interference Constraint

21. we can restrict each link to be assigned no more than channels (where ). This leads to the following constraint: Link-Channel Constraint

22. Node-Radio Constraint The number of established links at each node is constrained by the number of its radio interfaces. This leads to the following constraint:

23. Additional Constraint In addition, we can add a node-connectivity constraint to make sure that each node has established at least (where ) links. This leads to the following constraint:

24. Problem Formulation This problem can be formulated as: Subject to:

25. Numerical Results • Two types of scenarios: • homogeneous node location • heterogeneous node location TABLE II NOTATIONS AND PARAMETER SETTINGS

26. Scenario I: Homogeneous Node Location(1/3) Fig. 1. (a) Homogeneous node location

27. Scenario I: Homogeneous Node Location(2/3) TABLE III SET OF AVAILABLE CHANNELS AT EACH NODE TABLE IV HETEROGENEOUS TRANSMISSION RANGES

28. Scenario I: Homogeneous Node Location(3/3) Fig. 3. (a) Homogeneous node location

29. Scenario II: Heterogeneous Node Location(1/6) Fig. 1. (b) Heterogeneous node location

30. Scenario II: Heterogeneous Node Location(2/6) TABLE V SET OF AVAILABLE CHANNELS AT EACH NODE TABLE VI HETEROGENEOUS TRANSMISSION RANGES

31. Scenario II: Heterogeneous Node Location(3/6) They group the nodes into four 20m clusters. Fig. 2(a) and Fig. 2(b) show the intra-cluster links and inter-cluster links. Fig. 2. Possible links (i.e., E) for heterogeneous node location

32. Scenario II: Heterogeneous Node Location(4/6) In the BILP formulation, the objective function is to establish as many links as possible to increase channel reuse. As long as a channel is assigned to a link, the objective function is increased by 1. establishing an inter-cluster link is a must to guarantee the connectivity between the clusters. let denote the set of clusters, denote the set of nodes belonging to the -th cluster and denote the inter-cluster links between the -th and -th clusters .

33. Scenario II: Heterogeneous Node Location(5/6)

34. Scenario II: Heterogeneous Node Location(6/6) Fig. 3. Optimal solution on spectrum sharing

35. Conclusion • They model that: • Channel heterogeneity • Interference constraints • Spectrum sharing • Formulate an optimization problem in the form of binary integer linear programming (BILP). • Numerical results show that the optimal solution on spectrum sharing is highly dependent on the channel heterogeneity. • Future work will consider how to manage power control for heterogeneous channels.

36. Thanks for Your Attention!