2017 International Conference on circuits Power and Computing Technologies [ICCPCT] Prominence of Cooperative Communication in 5G Cognitive Radio Systems G. Shine Let G. Josemin Bala, J. Jenkin Winston M. Deepak Raj C. Benin Pratap ECE-Department of Electrical Technology, Karunya University, Coimbatore, India. firstname.lastname@example.org ECE-Department of Electrical Technology, Karunya University, Coimbatore, India. ECE-Department of Electrical Technology, Karunya University, Coimbatore, India. ECE-Department of Electrical Technology, Karunya University, Coimbatore, India. EEE-Department of Electrical Technology, Karunya University, Coimbatore, India. Abstract— Cognitive Radio is a promising way to overcome the spectrum scarcity for wireless communication and improving the spectral efficiency by using the vacant licensed spectrum band. Cooperative communication is a new communication technique which utilizes the help of neighboring nodes to reduce the bit error rate (BER) in a harmful fading environment. The challenging factor is to combine the cooperative communication in cognitive radio to improve the spectral efficiency and to reduce the BER factor of unlicensed user’s communication. In this paper, a comparative study of different communication techniques are done by considering Rayleigh fading channel environment and the advantages of cooperative communication is analyzed. Also, the paper deals with the challenges of integrating cooperative communication in cognitive radio network are discussed. Keywords—Cognitive Radio, Cooperative Communication, Decode-and-Forward protocol I. INTRODUCTION To efficiently utilize the frequency spectrum and to moderate the probability of error in new generation mobile communication systems new techniques are considered with the existing techniques. To resourcefully use the licensed spectrum, cognitive radio concept was introduced by Simon Haykin in . To moderate the probability of error, cooperative communication was introduced by  for wireless network. The cognitive radio and cooperative communication concept can be combined for 4G and 5G mobile communication system. In cognitive radio networking, the mobile devices equipped with cognitive radio will able to access the licensed spectrum when the licensed user is not accessing the spectrum. Research on cognitive radio started due to the wastage of spectrum in allotted TV band. Based on the spectrum occupancy survey taken in New York City approximately 80% of the spectrum is not utilized [3-4]. As day goes by, the number of mobile users increases tremendously. To minimize the probability of Bit-Error-Rate (BER) different diversity techniques such as time-diversity, frequency diversity, spatial diversity etc. are considered in the communication scenario. Out of many diversity techniques spatial diversity technique effectively addressed the performance degradation due to multipath fading. This diversity technique does not consider any time delay or bandwidth expansion for improving system efficiency. In mobile communication, employing spatial diversity in base station seems easier. But in mobile stations, including more 978-1- 5090-4967- 7/17/$31.00 © 2017 IEE number of antennas is not possible because of its size and hardware complexity. Having more number of antennas in transmitter and receiver is called as MIMO (Multiple-Input Multiple-Output). Implementing MIMO concept in mobile finds it difficult by the researchers. To overcome this difficulty, Cooperative Communication was emerged and it has the benefits of MIMO. Main advantage in cooperative communication is many mobiles with single antenna will share their antenna to efficient communication. Thus, the cooperative communication is also called as “virtual-MIMO” . In 4G and 5G mobile communications, cooperative communication plays a vital role for the improvement of mobile user’s communication in a fading environment. The different ways of cooperative communication and its performance is shown in the following sessions. II. IMPORTANCE OF COOPERATIVE COMMUNICATION For cooperative communication, neighboring nodes will help the sender to forward the data to the destination. Neighboring nodes are also called as relay nodes. The relay node will perform amplify-and-forward, decode-and-forward or compress-and-forward. In Amplify-and-forward (AF) protocol , the relay node amplifies the received data and forwards to the destination. Due to wireless scenario, the data received by relay node is affected with noise. The data along with noise is amplified and forwarded to destination using AF protocol. This is the major drawback of AF protocol. In Decode-and-forward (DF) protocol , the data received is decoded by the relay node. If the data is decoded correctly, the information is forwarded to the destination. For dynamic traffic and channel conditions, DF protocol performs well compared to AF protocol. Also, DF protocol is simple to implement. In Compress-and-forward (CF) protocol , the relay node compresses the received data and encodes it into a new data and forward to destination. This protocol increases security and capacity in the transmission link. A. BER performance in Direct Communication Now-a-days in mobile communication, the sender transmits the data to base-station in wireless medium. From base-station (BS) the data is forwarded to Mobile Switching Centre (MSC) and this connection is wired. 2017 International Conference on circuits Power and Computing Technologies [ICCPCT] Here for simulation, QPSK modulated signal is transmitted from source to destination wirelessly. Rayleigh fading channel is considered. Fig 1 shows the direct communication scenario. Fig:3 SIMO Communication Fig 1: Direct Communication Scenario The received signal at the destination is given by, ysd = √P hsdx + wsd (1) where ‘P’ is the transmit power at the source, ‘hsd’ is the source destination channel coefficient which have Rayleigh distribution, ‘x’ is the transmitted symbol (either ‘-1’ or ‘1’), ‘wsd’is the Gaussian noise. The probability density function of Rayleigh fading environment is given by (2) where ‘a’ is the envelope of fading channel and ‘δ2’ is the average power of the fading channel. Fig 2 shows the probability of symbol error rate (SER) vs signal-to-noise (SNR) ratio for direct communication. The received signal at the destination by antenna-1 is given by, y1 = √P h1x + wsd1 (3) The received signal at the destination by antenna-1 is given by, y2 = √P h2x + wsd2 (4) where, ‘h1’& ‘h2’are the channel coefficient which have Rayleigh distribution between source – destination antenna-1 and source – destination antenna-2 respectively, ‘wsd1’ & ‘wsd2’are the Gaussian noises in anteena-1 and antenna-2 respectively. Fig: 4 shows the probability of symbol error rate (SER) vs signal-to-noise (SNR) ratio for SIMO communication. 0 10 SIMO Communication -1 10 0 10 SER Direct Communication -1 -2 10 SER 10 -3 10 -2 10 -4 10 0 2 4 6 8 10 12 SNR in dB 14 16 18 20 -3 10 0 2 4 6 8 10 12 SNR in dB 14 16 18 20 Fig:2 SER vs SNR (dB) in Direct Communication B. BER performance in SIMO Communication SIMO communication means Single-Input Multiple-Output communication where single antenna is used in transmitter side and multiple antennas are used in receiver. By using multiple antennas in the transmitter and receiver side, the fading effect due to wireless environment can be reduced and thus the biterror rate of the received signal can be reduced. For simulation, two antennas at the receiver are considered. Compared to the direct communication the bit-error rate is reduced. But, practically having multiple antennas in mobile is difficult. Now-a-days mobile device thickness is very small. So integrating many antennas within small area is difficult. If multiple antennas are integrated in one device the selfinterference will be more. Fig: 3 shows the simple SIMO communication system considered for simulation. Fig:4 SER vs SNR (dB) in SIMO Communication C. BER performance in Cooperative Communication using AF protocol In cooperative communication, the source information is forwarded by intermediate nodes apart from direct communication. Here the relay node amplifies the received message and then forwards to destination. Fig 5 shows the cooperative communication using one relay node. Fig 5: Cooperative Communication using Relay node 2017 International Conference on circuits Power and Computing Technologies [ICCPCT] In Amplify-and-Forward based cooperative system, during phase-1 the source transmits the information to destination and relay. In phase-2, relay amplifies the received signal and forwards it to the destination. The received signal at the destination and relay at phase-1 is given by, ysd = √P hsdx + wsd (5) (6) ysr = √P hsrx + wsr ysd = √P hsdx + wsd ysr = √P hsrx + wsr In phase-2, the relays decode the received signal. The relay which decoded the data correctly re-encodes and forwards to the destination. In destination, maximum ratio combiner is used to combine the received signal from source and relays. The received signal at the destination in phase-2 is given by equation (10) yrd = √P1 hrdx+ wrd The received signal at the destination at phase-2 is given by, yrd = √P1 hrd 1 2 P | h sr | + N 0 + wrd (7) where, P and P1 are the transmit power at the source and relay respectively. Fig: 6 shows the probability of symbol error rate (SER) vs signal-to-noise (SNR) ratio for AF protocol cooperative communication. (8) (9) (10) Fig 8 shows the probability of symbol error rate (SER) vs signal-to-noise (SNR) ratio using selective DF protocol in cooperative communication. 0 10 Selective DF Protocol -1 10 0 10 AmplifyForward Communication -2 10 -1 SER 10 -3 SER 10 -2 10 -4 10 -3 10 -5 10 -4 10 0 2 4 6 8 10 12 SNR in dB 14 16 18 20 Fig 6: SER vs SNR (dB) in Cooperative Communication using AF protocol D. BER performance in Cooperative Communication using DF protocol Consider multiple relays are used for cooperative communication as shown in fig 7. Here each relay uses decodeand-forward protocol. The relay which decodes the received data correctly, re-encodes the data and forward to destination . Fig 7: Cooperative Communication using multiple relay nodes In phase-1, source broadcasts its information to the destination and relays. The received signal at the destination and relays is given by equation (8) and (9) 0 2 4 6 8 10 12 SNR in dB 14 16 18 20 Fig 8: SER vs SNR (dB) in Cooperative Communication using Selective-DF protocol III. COMPARATIVE STUDY The performance of different protocol is analyzed by transmitting the data through Rayleigh fading channel. TABLE I. COPERATIVE COMMUNICATION COMPARITIVE STUDY Communication Types Symbol Error Rate(SER) for SNR 20dB Direct Communication 9.48 x 10-3 Single-Input Multiple-Output (SIMO) communication Cooperative Communication using AF protocol Cooperative Communication using Selective-DF protocol 2 x 10-4 8 x 10-4 9 x 10-5 Table-1 shows the comparison of different ways of communication with respect to symbol error rate (SER). From the tabulation, it has been same the SER due to SIMO communication and cooperative communication using selective-DF protocol is almost same. In SIMO communication, multiple antennas have to be integrated in one device. Placing the antenna properly in the device by minimizing self-interference is difficult. But, the same SER is achieved by having cooperation between intermediate devices. 2017 International Conference on circuits Power and Computing Technologies [ICCPCT] IV. COOPERATIVE COMMUNICATION IN COGNITIVE RADIO NETWORK Spectrum sensing is the main challenge in cognitive radio network. The unlicensed users are also called as secondary users have to first find the vacant spectrum before communication. To find the vacant spectrum with less probability of error, neighboring nodes information is considered. Thus cooperative communication techniques are used in cognitive radio spectrum sensing. By using cooperative sensing, false alarm probability and hidden terminal problem can be reduced and signal detection accuracy can be improved. Cooperative spectrum sensing is classified as centralized, distributed and relay-assisted spectrum sensing and various parameters are analyzed in different techniques. In , sensing diversity gain is analyzed by using cooperative spectrum sensing in cognitive radio network. In , paper deals with different decision fusion rule in cooperative spectrum sensing. The performance of OR rule gives better results in many practical scenarios. The different challenges in cooperative spectrum sensing are to have control channel for sensing, increase in sensing time, and energy considerations. Apart from cooperative sensing, cooperative data transmission can also be done in cognitive radio network among unlicensed users. In , a half-slotted ALOHA multiple access protocol is used for the data transmission. Time slot is allotted for primary and secondary user communication. In this paper , secondary user transport capacity is analyzed according to the coverage radius. carried out in cognitive radio network, to have high quality-ofservice for secondary users’ communication. V. CONCLUSION Cooperative communication in cognitive radio network seems to be more prominence in 5G wireless communication. This overcomes the effect of under spectrum utilization, fading effect, path loss, shadowing etc. This paper gives the comparison of different cooperative communication techniques simulated in Rayleigh fading environment. Selective Decodeand-forward protocol gives 9x10-5 symbol error rate compared to other protocols. Also, the paper gives some insights on challenges in having cooperative communication in cognitive radio network as a future work. REFERENCES      A new algorithm was proposed in  by combining DF and AF protocol and it is named as Hybrid-Decode-AmplifyForward (HDAF). Outage probability is analyzed by considering different interference constraint. The best relay performance is selected to reduce the interference effect from primary users. The outage probability can be reduced by considering more number of potential relay. The performance of cognitive radio network has been analyzed by considering multiple communications between secondary users as future work in .  A cooperative communication is done between devices under a base station using selective-relay protocol. To deal with relay selection and efficient resource sharing, bar-gaining game is introduced in . The system efficiency and fairness are analyzed. This approach can be used for efficient secondary user’s communication in cognitive radio network. In , outage performance of cognitive radio system is demonstrated by considering cooperative relay system and MIMO system. Compared to SISO, the performance of the system gives better outage.  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