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

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

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 3

Introduction (cont’d) 4  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

Introduction (cont’d) 5  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.

Introduction (cont’d) 6  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 effects when multiple transmissions happen simultaneously on one channel.

Introduction (cont’d) 7  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 [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

Problem Definition 8  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

Problem Definition (cont’d) 9  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.

Problem Definition (cont’d) 10  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

Problem Definition (cont’d) 11  Maximizing spectrum utilization  The objective function is find out the maximum total number of CPE served Let a c i be 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.

Problem Definition (cont’d) 12  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: (i 1, i 2,... i m ).  Feasibility means there exists a set of positive transmit power levels P c = (P c i1, P c i2,..., P c im ) T all the SINR constraints of the m CPEs are met while the interferences caused to PUs do not exceed the acceptable threshold.

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

Problem Definition (cont’d) 14  Two-step Feasibility Check:  Step 1:  Check if the maximum eigen-value of matrix F c 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 P c ∗.  Then, check if P c ∗ satisfies the constraints for protecting PUs in (7) and the maximum power constraints in (8).  If yes, conclude that the assignment is feasible and P c ∗ is the power vector that should be used. Otherwise, the assignment is not feasible.

Channel-Allocation/Power-Control Algorithms 15  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.

Channel-Allocation/Power-Control Algorithms (cont’d) 16  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 ∗.

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

Numerical Results and Discussion 18  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 N o = − 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.

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

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

Conclusion 21  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

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