1 11 Device-centric Spectrum Management Haitao Zheng University of California, Santa Barbara Lili Cao Shanghai Jiaotong University, Shanghai, P.R. China DySPAN 2005

2 22 Outline Introduction Related Work Rule Based Spectrum Management Spectrum Management Rules Experimental Results Conclusions

3 Open Spectrum System Traditional spectrum management schemes license fixed spectrum slices to each wireless technology Studies find more than 70% of spectrum is unutilized in most areas [15] One solution is the use of open spectrum systems While primary users have priority in spectrum access, secondary users opportunistically use available spectrum without interfering with primary users A critical problem in open spectrum systems is efficient spectrum management for secondary users Maximizing utilization is the primary goal [15] MCHENRY, M. Spectrum white space measurements. New America Foundation Broadband Forum (June 2003)

4 Collaborative Approach Our previous work on decentralized spectrum allocation [6], [18], [25] has shown that user collaboration leads to results that closely approximate the optimal allocation Collaboration requires users to be somewhat selfless, sacrificing local performance to improve overall system utility It also requires coordination and frequent information exchange among users Collaborative model may heavily stress the communication resources of constrained networks such as sensor and mobile ad hoc networks [6] CAO, L., AND ZHENG, H. Distributed spectrum allocation via local bargaining. In IEEE SECON (Santa Clara, CA, Sept. 2005) [18] PENG, C., ZHENG, H., AND ZHAO, B. Y. Utilization and fairness in spectrum assignemnt for opportunistic spectrum access. Mobile Networks and Applications (MONET) (2006) [25] ZHENG, H., AND PENG, C. Collaboration and fairness in opportunistic spectrum access. In Proc. 40th annual IEEE International Conference on Communications (June 2005)

5 Device-centric Spectrum Management This paper proposes an alternative device-centric spectrum management scheme for devices with constrained communication resources Users observe local interference patterns and act independently according to preset spectrum rules This approach greatly simplifies allocation and significantly reduces control traffic

6 Existing Work on Spectrum Management Previous work took a collaborative approach, where secondary users negotiated spectrum with neighbors in order to maximize system utility as defined by optimization objectives such as fairness and utilization [6], [18], [25] In [6], users affected by the mobility event self-organize into bargaining groups and adapt their spectrum assignment [18], [25] reduce the problem to a variant of graph coloring problem These collaboration-based approaches share the property that neighbors exchange information frequently High implementation complexity and communication overhead They are not ideal for resource constrained secondary users

7 Spectrum Management System

8 Network Model We consider a network with some primary users N users (secondary users) M orthogonal noninterfering channels Each user n keeps track available channels: L(n) neighboring users: d(n) We assume each channel has similar throughput capacity, e.g. 1 each user transmits using a predefined combination of operating parameters (power, modulation, etc.) and there is no power control no two neighbors modify their spectrum/channel usage simultaneously

9 Rule A (Uniform Idle Preference) (1) We start with a simple rule: to prevent interference, users always select idle channels A channel is idle if the spectrum report shows no activity during the previous time period of length X where X is a design parameter To provide fairness, we limit the number of channels each user can access For user i, the total number of channels occupied by its neighbors is at most, but

10 Rule A (Uniform Idle Preference) (2) A small number of users experiencing intensive interference from primary users (small L(n)) or other secondary users in a crowded area (large d(n)) can limit the value of Ω leading to less than ideal spectrum utilization adapting Ω to each user’s interference condition is preferred The work in [6] showed that collaborative spectrum allocation guarantees a minimum number of the channels each user n can get Referred to as the poverty line the bound is proportional to each user’s interference condition

11 Rule B (Poverty Exact Idle Preference) (1) If number of idle channels < PL(n), it “grabs” channels from “richer” users without impacting “poor” users A user conflicting with a “poor” user will give up the channel and switch to other channels following the same procedure To n, a neighbor is “richer” if it uses more channels than n; otherwise it is “poor” To “grab” non-idle channels, a user n marks the channels occupied by “poor” neighbors as busy, and the rest as idle User n then selects a set of channels from the “idle” channels until its channel occupancy reaches PL(n)

12 Rule B (Poverty Exact Idle Preference) (2) Rule B requires that each user has knowledge of the number of neighbors d(n), and the channel selection of each neighbor in order to identify “richer” users A limitation of Rule B is that each user only attempts to use PL(n) channels Since PL(n) represents a lower bound on spectrum usage derived using a collaboration based approach [6], Rule B could under- utilize available spectrum

13 Rule C (Poverty Guided Idle Preference) (1) max{ 0, min{ C(r) − PL(n), PL(n) − C(n)} } where C(n) and C(r) are the current spectrum usage of user n and r Rule C allows users who have attained their poverty line to grab additional idle channels users below their poverty line to grab channels from “richer” neighbors Rule C does not require each user to have knowledge of its neighbors’ poverty line

14 Rule C (Poverty Guided Idle Preference) (2) Assuming no two neighbors modify their spectrum/channel usage simultaneously, the system will reach equilibrium after a finite number of iterations Equilibrium is the state where users have no incentive to adjust spectrum usage

15 Proof of Theorem 2 (1) Property 1 A node n can find at least PL(n) channels, which do not lead to “poverty based conflict” with neighbors whose poverty line is equal or less than that of n “poverty based conflict” refers to conflict with neighbors whose channel usages are below their poverty line Proof for Rule B (select exactly PL(n) channels. If not enough, grab channels from richer neighbors) There are at most d(n) neighbors of n with less or equal Poverty Line than n denoted n 1, n 2,..., n i, i ≤ d(n) where C(n) represents the spectrum usage of user n So at least channels are available for n to use

16 Proof of Theorem 2 (2) Proof for Rule C (select all idle channels. If not enough, grab channels from richer neighbors) A user n can reserve PL(n) channel(s) for each neighbor, regardless of their Poverty Line We define a user as “qualified” if its channel usage does not conflict with any of its neighbors with poverty line equal or less than that of user n “disqualified”, otherwise Based on Rule B and C, only “disqualified” users modify their channel usage A user conflicting with a “poor” user will give up the channel and switch to other channels following the same procedure Based on Property 1, after modifications, a “disqualified” user becomes “qualified”

17 Proof of Theorem 2 (3) Property 2 When a user n modifies its channel usage and transfers any of its neighbors, i.e. user n 1 from “qualified” to “disqualified”, then PL(n) < PL(n 1 ) If PL(n) ≥ PL(n 1 ), then after the modification n won’t conflict with n 1, so n 1 won’t change from “qualified” to “disqualified” Proof for Theorem 2 At an equilibrium, each user has no incentive to modify its spectrum usage By Property 1, at an equilibrium, for all n, |C(n)| ≥ PL(n), and the channel selection of n does not conflict with its neighbors with equal or lower Poverty Line compared to PL(n) The whole system is conflict-free and each user’s spectrum usage reaches its Poverty Line

18 Proof of Theorem 2 (4) If a user with lower poverty line than its neighbors modifies its channel usage first each “qualified” user will never become a “disqualified” user after its own spectrum modification the number of iterations is at most N When spectrum modification is disordered, we can use induction to prove that the maximum number of iterations is bounded by O(N 2 )

19 Additional Mechanisms Users need to know the set of channels each neighbor currently occupies by broadcasting channel usage in beacon broadcasts or routing hello messages Rule B and C assume no simultaneous spectrum adjustments by neighboring users After a user decides to switch to a new channel, it computes a short random wait time before starting transmissions If it detects activity on the channel during the wait time, it marks the channel as busy, and the channel switch is cancelled

20 Rules for Contention-based Channel Assignment Broadcasting spectrum usage to neighbors might be undesirable for a number of reasons privacy concerns and protection against jamming from malicious users Rules that do not require knowledge of neighbors’ spectrum usage When a user below its poverty line can not find any idle channel, we can let users share channels through a random access based approach Users could be selfish and occupy all the channels, reducing the system to a single channel with full interference We need to regulate the maximum number of channels each user can use

21 Rule D and Rule E The poverty line concept can provide a reference for choosing different value of Ψ for different users Users monitor channel conditions and switch to channels that provide the best throughput We use the number of competing users as an indicator of channel quality

22 Theorem 3 and 4 When there are m users sharing a channel, each user gets 1/λ·m of channel throughput where λ is the contention penalty It only guarantees a lower bound on |L(n)| = M The bound is only tight when Ψ = M and cannot be used to derive the optimal value of Ψ

23 Simulation Environment Randomly placing users on 100×100 area Two users conflict if they are within distance of 20 Mobility is simulated as in each time instance, 20% users move to another randomly selected location We assume λ = 1 for Rule D and E Performance metric Utilization  Assuming β n is user n’s throughput over its selected channels Fairness

24 Utilization Comparison of Rule A and B

25 Comparison of Rule D and E with Bargaining Scheme

26 Utilization and Fairness Comparison of Different Rules

27 Complexity Comparison of Different Rules

28 Conclusions Propose a device-centric spectrum management scheme Users act independently based on local observations and spectrum rules Resulting in significantly lower communication between users Propose five rules that tradeoff performance with implementation and communication complexity Show that rules guided by a lower-bound calculation (poverty line) Show that rule-based approaches perform slightly worse than the previously proposed collaborative approaches but have much lower complexity and communication overhead