Presentation on theme: "MAXIMIZING SPECTRUM UTILIZATION OF COGNITIVE RADIO NETWORKS USING CHANNEL ALLOCATION AND POWER CONTROL Anh Tuan Hoang and Ying-Chang Liang Vehicular Technology."— Presentation transcript:
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 1
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  and  The objective of  is to maximize spectrum utilization,  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  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.  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.
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.  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