CDMA Based Cognitive Radio Cellular Network by Sanjay Dhar Roy s_dharroy@yahoo

1. CDMA Based Cognitive Radio Cellular Network by Sanjay Dhar Roy s_dharroy@yahoo.com ECEDepartment National Institute of Technology, Durgapur, India

2. Introduction on Cognitive Radio (CR) and CR Network • Outage Analysis in Frequency Planned Cognitive Radio Network • Spectrum Sensing in CR-CDMA Networks with Co-located BSs • On the Data Services of Secondary User with Primary Exclusive Region

3. INTRODUCTION • Cognitive Radio (CR): “Cognitive radio (CR) is a form of wireless communication in which a transceiver can intelligently detect which communication channels are in use and which are not, and instantly move into vacant channels while avoiding occupied ones. This optimizes the use of available radio-frequency (RF) spectrum while minimizing interference to other users.” • WHY COGNITIVE RADIO? • Demand for wireless communication capacities continuously growing • Bandwidth is expensive and good frequencies are taken • Occupancy of spectrum (below 1 GHz) is around 6~10%. • Spectrum holes • CRN: A network that has CR as the main technology. CRN is mostly used in association with a PRN. Present and future generation heterogeneous networks use CR technology for efficient spectrum utilization.

4. INTRODUCTION • Spectrum Sensing: Monitoring spectrum usage by primary users (PUs). Enables CR users to adapt to the environment by detecting spectrum holes. • Spectrum Decision:Capability to decide the best available bands according to QOS requirements. • Spectrum Sharing: Coordinates transmission attempts between CR users to avoid collision • Spectrum Mobility: Ensures smooth and fast transition during spectrum handoff.

5. Outage Analysis in Frequency Planned Cognitive Radio Network BS6 BS1 D/2 BS0 D BS2 BS5 r00 R0 BS4 BS3 PU SU Fig: Hexagonal CR-CDMA networks model with seven BSs. Fig : Frequency planned cellular-cognitive radio networks.

6. System Model: Analytical modeling The average power without shadowing at a desired PU (whose performance is being measured) from BS0 can be expressed as follows: The SIR expression for the desired PU: for i = 1 to

7. 0.014 r=0, analytical r=0.5, analytical r=0.9, analytical 0.012 r=0.9, simulation r=0.5, sig =8dB 0.01 r=0.5, sig=8dB, P =0.125 c o g P =0.125, simulation c o g 0.008 r=0.5, d1=D/2-50 Probability of outage 0.006 0.004 0.002 0 50 100 150 200 250 300 350 400 Number of Secondary users Fig : Outage probability as a function of the number of cognitive users.Results with cluster size (Nq) = 7 Pout (i) Pcog (ii) r (iii) shadowing

8. 0.045 r =400, sig =6dB, d =D/2-10, pg=64 0 0 1 0.04 r =400, sig =6dB, d =D/2-10, pg=32 0 0 1 r =400, sig =6dB, d =D/2-20, pg=64 0 0 1 0.035 r =300, sig=6dB, d =D/2-10, pg=64 0 0 1 r =300, sig=8dB, d =D/2-10, pg=64 0.03 0 0 1 0.025 Probability of outage 0.02 0.015 0.01 0.005 0 10 20 30 40 50 60 70 Number of Secondary users , Fig: Probability of outage for a PU vs. the number of SUs. Results withNq = 1; Desirable-> Higher PG; smaller r00

9. Spectrum Sensing in CR-CDMA Networks with Co-located BSs • Performance of Cognitive Radio (CR) CDMA networks is analyzed. • A simulation test bed for analyzing the performance of a CR user with and without spectrum sensing in a three cell scenario is developed. • CR users belong to a Cognitive Radio Network (CRN) which coexists with a Primary Radio Network (PRN). Both CRN and PRN are CDMA-based. • Three different schemes for spectrum sensing are considered (spectrum underlay). • Soft Handoff (HO) and power control are considered for both CRN and PRN. • Performance in terms of: (1) outage probability, (2) blocking probability, (3) average data rate of secondary users (SUs).

10. Fig: Hexagonal multi-cellular model with three BSs labelled as 0, 1, 2.

11. System Model (contd.) • Three-cell CDMA network. Each cell with co-located primary BS and secondary BS. Each cell has three sectors. • Fixed number of PUs and fixed number of SUs are considered. Multi code (MC)-CDMA is assumed for PUs and SUs. • BS distinguishes transmissions from SU and PU by cyclostationary measurements. • Interference due to SUs and PUs at SBS is estimated. SINR at SBS for the desired SU is estimated. Performance of a SU is evaluated on the basis of this SINR. • Performance of a SU has been measured in terms of blocking probability and achievable data rate.

12. System Model (contd.) • Scheme 1: • Equal number of two types of PUs. • Interference limit Imax (in uplink) determined in the absence of SUs. • A basic data rate for PUs. • At any time, total interference caused by SUs and PUs must be less than this interference limit. • Scheme 2: • Network allows some amount of interference from SUs as well, in the presence of PUs. • The tolerable amount of interference is limited by the ratio, Imax/ucp. • The other main network parameters are set as in Scheme 1.

13. Simulation Model Generation of Users’ Locations and Interference Powers: Fixed numbers of users (PUs and SUs) are generated. Link gains corresponding to each user are generated. Corresponding interference at BS is also generated. Interferences are generated from all users at non handoff region, soft handoff region, and from users of other sectors. Probability of Outage with Spectrum Sensing: The ratio between the interference power due to SUs’ activity and the interference power due to PUs’ activity is evaluated at SBS. If the condition is not satisfied, then SUs are removed one by one, initially from the non-HO region of BS0 (region ‘A’) and then from other zones i.e., regions ‘B’,…,‘H’. Here is the ratio of the interference powers of SUs and PUs. Finally, outage probability is evaluated.

14. 0.14 PU1=5, PU2=5, r =7 kbps, Scheme1 d PU1=5, PU2=5, r =7 kbps, Scheme2 d 0.12 Ghavami no SS Ghavami SS 0.1 Ghavami SS and BF 0.08 Probability of Outage 0.06 0.04 0.02 0 5 5.5 6 6.5 7 7.5 8 8.5 9 Number of Cognitive Users Fig: Probability of outage for a SU as a function of the number of SUs. Pout if Nsu

15. 0.14 PU1=5, PU2=5, r =7 kbps, Scheme1 d PU1=5, PU2=5, r =7 kbps, Scheme2 d 0.12 Ghavami no SS Ghavami SS 0.1 Ghavami SS and BF 0.08 Probability of Outage 0.06 0.04 0.02 0 5 5.5 6 6.5 7 7.5 8 8.5 9 Number of Cognitive Users Fig: Probability of outage for a SU as a function of the number of SUs. Pout if Nsu

16. Fig. Blocking Probability for SUs as a function of the number of PUs, with fixed number of SUs and fixed value of SUs’ data rate. Pblk Npu ; Scheme 2 is better than Scheme 1 in terms of Pblk .

17. Fig. Average data rate of SUs as a function of the number of SUs with fixed numbers of PUs and various values of SUs data-rate for Ghavami SS. Data rate if Nsu or rd

18. Major Remarks • The proposed simulation model allows fast performance evaluation of a Cognitive Radio CDMA network. • As expected, spectrum sensing can improve the performance. • Scheme 2 outperforms Scheme 1 in terms of SU blocking probability for any given fixed number of SUs and this improvement is more pronounced for large numbers of PUs. • The SU performance, in terms of outage and blocking probabilities, improves if the data rate of SUs decreases. • A larger number of cognitive users degrade the SU performance, in terms of outage and blocking probabilities, for fixed number of PUs.

19. On the Data Services of Secondary User with Primary Exclusive Region

20. This work • Performance of Cognitive Radio (CR) CDMA networks is analyzed. • A simulation test bed for analyzing throughput, delay performances of a SU in a single cell, where both SUs and primary users (PUs) are present. PUs are inside the primary exclusive region. • CR users or SUs forming pair wise links belong to infrastructure less ad hoc network, which coexist with a Primary Radio Network (PRN). • Spectrum underlay type of communication where SUs transmit in presence of PUs maintaining a predefined interference limit for PRN. • A SU transmits packetized data for its corresponding receiver. • A stop and wait ARQ protocol at MAC layer for packet retransmission by the SU of interest.

21. System Model Fig. One cell model with a number of PUs and SUs. PUs are power controlled by the BS at the center of the cell. SU receiver corresponding to each SU transmitter is within the circle of radius of 0.5dadhoc. The inner circle is exclusively for PUs only. SUs are allowed inside the annular ring i.e., in between the circles with radii d0 andD.

22. System Model (contd.) • The network with a number of PUs and SUs uniformly distributed inside the cell. • All users (both PUs and SUs) have omni directional antennas for transmission. • A PU is power controlled by the BS. Similarly, a SU transmitter is assumed to be power controlled with respect to its corresponding SU receiver. • A “continuously active” data traffic model as in [7] is considered for each SU link where each SU generates a sequence of fixed length packets. A new packet is generated as soon as the preceding packet is delivered successfully. • A SU is allowed to transmit, if interference at BS for the PU link of interest is below a predefined interference threshold. Up link interference at BS for a PU is due to all other PUs other than the desired one, and all SUs. QoS of any user is directly related to SINR of the user. Hence, we find SINR of a PU and SINR of a SU.

23. System Model (contd.) The SINR of a PU , in terms of bit energy to noise power spectral density, may be expressed as follows: The SINR at j-th SU receiver, The UL instantaneous interference for i-th PU : The retransmission probability Pr : Pr is simulated and corresponding throughput, delay are found using following eqns. Packet transmission time : Delay : Throughput :

24. Simulation Model A. Generation of Users’ Locations and Interference Powers: Fixed numbers of users (PUs and SUs) are generated. The locations of all SUs and PUs (with respect to the BS are generated within the cell. A number of SU receivers are generated around corresponding SU transmitters. For each of the users, the link gains corresponding to BS and SU links (e.g., link gains from PU to BS, SU to BS, PU to SU, SU to SU) are generated. The interference power received at the reference BS for the PU of interest is evaluated. Similarly, interference power for the SU link of interest is evaluated, considering interference from all other SU transmitters, and all PUs in the system. Next, the SINR for the SU is evaluated. Interference at BS for the PU is also evaluated. If this instantaneous interference is less than the interference threshold then the SU of interest will be allowed to transmit data.

25. Simulation Model (contd.) B. BER simulation of data : 1. A sequence of random data bits +1 or –1 is generated which indicates the transmitted bits. 2. A Gaussian noise sample is generated with variance and added to each transmitted bit, where is found following steps A (1) to A (5) fora given PG. 3. The received bit is first detected as +1 or –1 after comparing with a threshold of 0. Then each received bit is compared with corresponding transmitted bit and an error_count is incremented if they disagree. 4. Steps B(1) to B(3) are repeated to estimate BER.

26. Simulation Model (contd.) C. Packet error, Delay and Throughput simulation 1. A packet consisting of L (information bits are generated. A sample of Gaussian noise as in B(2) with processing gain (PG) as in B(2) is added to each transmitted bit of a packet. 2. The received L bits of a packet are checked with their corresponding transmitted bits to assess packet error. 3. If the received packet is incorrect, the same packet (i.e. same bit pattern as in C(1)) is retransmitted until the packet is received correctly finally. 4. Total number of erroneous packet is counted out of a large number of transmitted packets to estimate the PER. 5. Average delay (D) and throughput are estimated from retransmission probability.

27. Fig. PER vs. d0 (meter)

28. Fig. PER vs. d0(meter) for different number of users PER d0

29. Fig. Throughput vs. d0(meter) for different Pmax values d0 Throughput

30. Major remarks • We have proposed a framework for analyzing data performance of a SU operating in spectrum underlay. • Specifically, we have given more attention on the effect of changing transmit powers of PUs and SUs on SU’s performance. • We have shown impacts of PUs’ interference on SU by considering a PU’s exclusive region. If d0 decreases interference on SUs increases which further worsen SUs’ data performance. • Increasing upper limit of PUs’ transmit power has more detrimental effect on SUs’ performance than increasing upper limit of SUs’ transmit power.

31. References Hai Jiang, Weihua Zhang, Xuemin (Sherman) Shen, Qi Bi, “Quality-of-Service Provisioning and Efficient Resource Utilization in CDMA Cellular Communications”, IEEE J. Select Areas in Commun, Vol. 24, No. 1, January, 2006. Mitola, J., III   Maguire, G.Q., Jr., “Cognitive radio: making software radios more personal”, IEEE Personal Communications Magazine, Vol. 6, Issue 4, Aug, 1999, pp 13-18. Dong In Kim,  Long Bao Le, Hossain, E., “Joint rate and power allocation for cognitive radios in dynamic spectrum access environment”, IEEE Transaction on Wireless Communication Vol. 7, No. 12, December 2008, pp. 5517 – 5527. Haykin S, “Cognitive radio: brain-empowered wireless communications”, IEEE Journal on Selected Areas of Communications, Vol. 3, Issue 2, Feb. 2005, pp 201-220. Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Varun, Shantidev Mohanty, “A Survey on Spectrum Management in Cognitive Radio Network”, IEEE Communication Magazine, April 2008, pp 40 – 47 Bin Wang and Dongmei Zhao, “Performance Analysis in CDMA-based Cognitive Wireless Networks with Spectrum Underlay”, IEEE Globecom Proceedings, 2008, pp. 1 – 6. Joon Bae Kim, and Michael Honig, “Resource Allocation for Multiple Classes of DS-CDMA Traffic”, IEEE Transaction on Vehicular Technology, Vol. 49, No. 2, March 2000, pp. 506 – 519.

32. Thank You