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Anh Tuan Hoang and Ying-Chang Liang Vehicular Technology Conference, 2006. VTC-2006 Fall. 2006

MAXIMIZING SPECTRUM UTILIZATION OF COGNITIVE RADIO NETWORKS USING CHANNEL ALLOCATION AND POWER CONTROL. Anh Tuan Hoang and Ying-Chang Liang Vehicular Technology Conference, 2006. VTC-2006 Fall. 2006. Outline. Introduction Problem Definition Channel Allocation / Power Control Algorithms

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Anh Tuan Hoang and Ying-Chang Liang Vehicular Technology Conference, 2006. VTC-2006 Fall. 2006

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  1. MAXIMIZING SPECTRUM UTILIZATION OF COGNITIVE RADIO NETWORKS USING CHANNEL ALLOCATION AND POWER CONTROL Anh Tuan Hoang and Ying-Chang Liang Vehicular Technology Conference, 2006. VTC-2006 Fall. 2006

  2. Outline • Introduction • Problem Definition • Channel Allocation / Power Control Algorithms • Numerical Results and Discussion • Conclusion and Comments

  3. Introduction • Consider a cognitive radio (CR) network in which a set of base stations make opportunistic unlicensed spectrum access to transmit data to their subscribers • The objective of this paper • Maximize the spectrum utilization of the cognitive network while appropriately protecting primary users • Develop spectrum-allocation/power-control schemes

  4. Introduction (cont’d) • Pros and Cons for CR networks • By allowing opportunistic spectrum access, the overall spectrum utilization can be improved. • Transmission from cognitive networks can cause harmful interference to primary users of the spectrum. • Important design criteria for cognitive radio network • Maximizing the spectrum utilization and minimizing the interference caused to primary users

  5. Introduction (cont’d) • The operational constraints • The total amount of interference caused by all opportunistic transmissions to each PU must not exceed a predefined threshold • For each CPE, the received signal to interference plus noise ratio (SINR) must exceed a predefined threshold • The system utilization • The total number of CPEs that can be supported while meeting the above two constraints • The utilization maximizing problem can be structured as a linear mixed (0-1) integer programming.

  6. Introduction (cont’d) • However, solving for an optimal solution of the linear programming is NP-hard. • Propose a heuristic scheme for channel allocation and power control • This heuristic scheme’s concept is based on • Using a dynamic interference graph that captures not only the pair-wise but also aggregate interference effectswhen multiple transmissions happen simultaneously on one channel.

  7. Introduction (cont’d) • Works on channel allocation and power control problem • Model interference effects based on the SINR include [6] and [7] • The objective of [6] is to maximize spectrum utilization, • [7] is to minimize total transmit power to satisfy the rate requirements of all links. • Power control problems for concurrently interfering transmissions with the objective of guaranteeing SINR constrains • In this paper, they use Perron-Fronbeniuos theorem to check the feasibility of a particular channel allocation [6] A. Behzad and I. Rubin, “Multiple access protocol for power-controlled wireless access nets,” IEEE Transactions on Mobile Computing, vol. 3, no. 4, pp. 307–316, Oct.-Dec. 2004. [7] G. Kulkarni, S. Adlakha, and M. Srivastava, “Subcarrier allocation and bit loading algorithms for OFDMA-based wireless networks,” IEEE Transactions on Mobile Computing, vol. 4, no. 6, pp. 652–662, Nov./Dec. 2005.

  8. Problem Definition • System model • Number of channels: K • Number of primary users: M • CR Network consisting of B cells • Within each cell, there is a base station (BS) serving a number of fixed customer premise equipments (CPEs) • Number of CPEs: N • Considering the downlink situation in which data are transmitted from BSs to CPEs

  9. Problem Definition (cont’d) • Operational requirements • SINR requirement for CPEs: • is the SINR at CPE i. • is the channel gain from the BS serving CPE j to CPE i on channel c • is denoted as the transmit power for the transmission toward CPE i on channel c. Aggregate interference The inequality can be regarded as the minimum SINR to achieve a certain bit error rate (BER) performance at each CPE.

  10. Problem Definition (cont’d) • Protecting primary users • (zeta-bar) is the predefined tolerable threshold of primary user • is the channel gain from the BS serving CPE i to PU p on channel c • is denoted as the set of all Pus that user channel c • For each PU, the total interference from all opportunistic transmissions does not exceed a predefined tolerable threshold

  11. Problem Definition (cont’d) • Maximizing spectrum utilization • The objective function is find out the maximum total number of CPE served Let acibe a binary variable denoting whether or not channel c is assigned to the transmission toward CPE i. One CPE only can occupy a channel at a time. SINR Requirement for Active CPEs (δ is a relatively large constant) The Protecting Primary Users’ Constraint Maximum Power Constraint.

  12. Problem Definition (cont’d) • Feasible assignment • Let us deal with the question of whether it is feasible to assign a particular channel c simultaneously to a set of transmissions toward m CPEs: (i1, i2, . . . im). • Feasibility means there exists a set of positive transmit power levels Pc = (Pci1, Pci2, . . ., Pcim)T • all the SINR constraints of the m CPEs are met while the interferences caused to PUs do not exceed the acceptable threshold.

  13. Problem Definition (cont’d) The Pareto-optimal transmit power vector is 

  14. Problem Definition (cont’d) • Two-step Feasibility Check: • Step 1: • Check if the maximum eigen-value of matrix Fc defined in (10) is less than one. (From the Perron-Frobenious Theorem) • If not, conclude that the assignment is not feasible, otherwise, continue at Step 2. • Step 2: • Using (12) to calculate the Pareto-optimal transmit power vector Pc∗. • Then, check if Pc∗ satisfies the constraints for protecting PUs in (7) and the maximum power constraints in (8). • If yes, conclude that the assignment is feasible and Pc∗ is the power vector that should be used. Otherwise, the assignment is not feasible.

  15. Channel-Allocation/Power-Control Algorithms • Constructing an interference graph • To represent the interference between pairs of unserved CPEs. • Moreover, this interference graph must also take into account the aggregate interference • caused by transmissions that have been allocated channels in previous steps. • To implement the Dynamic Graph Based approach • At each step, for each unserved CPE i, • Calculate its node degree corresponding to a channel c and prior channel-allocation matrix Asgn.

  16. Channel-Allocation/Power-Control Algorithms (cont’d) • Node degree representation  Deg(i, c, Asgn) • Deg(i, c, Asgn) = ∞ if it is not feasible to assign channel c to user i while keeping all prior assignments. • If it is feasible, Deg(i, c, Asgn) is the total number of unserved CPEs • that can not be assigned channel c anymore when this channel is assigned to CPE i. • The algorithm then picks a CPE-channel pair [i∗, c∗] that minimizes Deg(i, c, Asgn) and assigns channel c∗ to CPE i∗.

  17. Channel-Allocation/Power-Control Algorithms (cont’d) [3] Pick up the best CPE from UnSrv [4-6] No more feasible CPE condition. [8-10] All CPEs are served. UnSrv is the set of unserved CPEs.

  18. Numerical Results and Discussion • Simulation Model • A square service area of size 1000×1000m in which a cognitive radio network is deployed. • Model an orthogonal frequency division multiple access (OFDMA) system • No = −100dBm. • The required SINR at each CPE is 15dB. • The maximum tolerable interference for each PU is 90dBm. For each BS, the maximum transmit power on each channel is Pmax = 50mW.

  19. Numerical Results and Discussion (cont’d) Performance in terms of no. of CPEs served versus no. of PUs. No. of BSs = 9, no. of CPEs = 40, no. of channels = 16. Performance in terms of no. of CPEs served versus no. of PUs. No. of BSs = 4, no. of CPEs = 40, no. of channels = 16.

  20. Numerical Results and Discussion (cont’d) Performance in terms of no. of CPEs served versus no. of PUs. No. of BSs = 16, no. of CPEs = 40, no. of channels = 4. Performance in terms of no. of CPEs served versus no. of PUs. No. of BSs = 9, no. of CPEs = 40, no. of channels = 8.

  21. Conclusion • Propose a heuristic channel-allocation/power-control algorithm • A realistic control framework is formulated to guarantee protection to primary users and reliable communications for cognitive nodes. • Future works • Consider fairness among CPEs • A joint network-admission/resource-allocation framework

  22. Comments • Feasibility Test •  Minimum degree greedy scheme to solve the problem • The lack of simulation

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