Filter Bank Multitone: A Physical Layer Candidate for Cognitive Radios P. Amini, R. Kempter, R.R. Chen, L. Lin, B. Farhang-Boroujeny
Outline 1) Cognitive Radio → Definition → What has already been proposed? 2) Channel Sensing/Spectrum Pooling 3) Physical Layer Solutions → OFDM → Filter bank methods 4) Payload Transmission, two corner stones: → Complete central control scheme → Fully Random scheme → Current work: best of both worlds 5) Conclusion
Cognitive Radio Motivation: • Vast majority of the available spectrum has been licensed • Given a geographical position, most of the licensed spectra are rarely used. Idea : • Secondary users (SUs) may use the spectrum when the primary users (PUs) are inactive Consequence SUs need to be intelligent: Use spectrum when it is free and give it up when PU starts transmission
What has already been proposed: Spectrum pooling Proposed systems for CR are based on on Multicarrier communication: Use Multicarrier Idea Communications for SU to fill “holes” in the spectrum T.A. Weiss and F.K. Jondral, “Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency,” IEEE Commun. Magazine, Vol. 42, No. 3, March 2004, pp. S8 - S14.
Outline 1) Cognitive Radio → Definition → What has already been proposed? 2) Channel Sensing/Spectrum Pooling 3) Physical Layer Solutions → OFDM → Filter bank methods 4) Payload Transmission, two corner stones: → Complete central control scheme → Fully Random scheme → Current work: best of both worlds 5) Conclusion
Spectrum Pooling • Spectrum pooling mechanism needs a channel sensing method that continuously senses the channel. • Channel sensing should be performed with a very high probability of correct detection (to assure very low probability of interference with PUs). • Weiss et al proposed a distributed spectrum pooling protocol where all the nodes participate in channel sensing
Spectrum Pooling/Channel Sensing Protocol • All SUs perform detection • The distributed allocation vector is sent to the secondary user basestation • The base station combines the sensing information and sends it to all SUs Our system is based on this distributed spectrum pooling protocol Timo Weiss, Joerg Hillenbrand, Albert Krohn, Efficient Signaling of Spectral Resources in Spectrum Pooling Systems , Proc. of the SCVT 2003, Eindhoven, Netherlands
Outline 1) Cognitive Radio → Definition → What has already been proposed? 2) Channel Sensing/Spectrum Pooling 3) Physical Layer Solutions → OFDM → Filter bank methods 4) Payload Transmission, two corner stones: → Complete central control scheme → Fully Random scheme → Current work: best of both worlds 5) Conclusion
Methods of Multicarrier Communications: OFDM Advantage: • FFT at the receiver which is deployed for demodulation can also be used for spectrum sensing Disadvantage: • Large side lobes may lead to interference with PUs and among SUs. Solution: • Extension of each OFDM block with long cyclic prefix and suffix and apply windowing. Result 100% overhead T. Weiss, J. Hillenbrand, A. Krohn, F. K. Jondral, “ Mutual interference in OFDM-based spectrum pooling systems,” Vehicular Technology Conference, 2004. 2004. VTC 2004-Spring. 2004
Methods of Multicarrier Communications: Filter Multitone Filter Multitone (FMT): originally designed for DSL channels Advantages: • Presence of guard-bands between subcarriers allows flexible switching between different users • Simple to implement Result 25% Overhead G. Cherubini, E. Eleftheriou, S. Olcer, “ Filtered multitone modulation for VDSL ,” in Proc. IEEE Globecom ’99, vol. 2, pp. 1139-1144, 1999.
Multicarrier Communications: Cosine Modulated Multitone Cosine modulated Multitone (CMT): applied to DSL and wireless channels Advantages: • High bandwidth efficiency • Blind equalization No overhead B. Farhang-Boroujeny, “Multicarrier modulation with blind detection capability using cosine modulated filter banks,” IEEE Trans. Commun., vol. 51 ,no. 12, pp. 2057-2070, Dec. 2003.
Methods of Multicarrier Communications: Channel Sensing • In an OFDM based SU-system, FFT may be used for spectrum sensing But FFT has a leakage problem • Haykin proposed the multitaper method (MTM): no leakage problem But MTM is relatively complex Solution: We propose filter banks as an efficient tool for spectrum sensing. This is at almost no additional cost , since in our system, filter banks are running at the receiver for data transmission. S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal Selected Areas in Communications, vol. 23, no. 3, pp. 201-220, Feb. 2005.
Spectrum Sensing: Simulation Results Filter banks Dynamic range > 60 dB
Outline 1) Cognitive Radio → Definition → What has already been proposed? 2) Physical Layer Solutions → OFDM → Filter bank methods 3) Channel Sensing 4) Payload Transmission, two corner stones: → Complete central control scheme → Fully Random scheme → Current work: best of both worlds 5) Conclusion
Payload Transmission • So far, no protocol has been proposed for payload transmission in cognitive radios • We investigate the performance of the two corner stones of accessing schemes to gain a better understanding of the possibilities and limits of cognitive radio networks: 1) Complete central basestation control 2) Fully random accessing scheme A practical scheme will combine methods from these two for maximum efficiency and scalability
Payload Transmission: System Model • Channel sensing is performed every T=5 ms • SU channel access is restricted to time slots • The total bandwidth is divided into N=512 subcarriers • From these the number of subcarrier available to SUs is denoted by n(t) and is known after channel sensing. • With a bandwidth of B=400kHz for each subcarrier the total bandwidth available to SUs is C = n(t)B • The secondary and primary users are modeled as Poisson processes with aggregate arrival rates λ s and λ p, respectively
Simulation Scenario: System Model, cont’d • As the number of packets in the queues exceeds P =40 packets , the SUs enter the payload transmission stage • Packet lengths are trimodally distributed: Internet traffic • packet sizes=50 byte, p=0.5 • packet sizes=500 byte, p=0.4 • packet sizes=1500 byte, p=0.1
Simulation Scenario: Scheduled scheme • SU will only be accepted/can remain in the system if the guaranteed minimum rate R min =400kbps over T=5ms can be sustained • Let s denote the number of active secondary users and k is the maximum number of active SUs in the system: → if k < s, s-k users are backlogged → more users can be accepted if k > s • available bandwidth is assigned to SUs in round-robin fashion Ignoring signaling overhead, upper bound on SU data transmission
Throughput: Scheduled Scheme Pro: All available bandwidth can be used But: Scheduling may add significant overhead
Simulation Scenario: Multi-Carrier ALOHA • Active SUs randomly select W=10 subcarriers out of free subcarriers (multicarrier ALOHA) • As soon as one subcarrier has been chosen by more than one SU , we assume that the transmissions by all involved SUs are lost Most random, worst case scenario. Lower bound on performance of SU data transmission
Throughput: Random Scheme Problem: Very Inefficient, even when PU is “off”, collisions may happen between SU
Backlogged packets: Random Scheme Problem: Instability issues of fully random scheme
Comparison Pros Cons Central Control Excluding signaling, it Signaling overhead, allows to harness channel capacity Fully Random Very inefficient and No signaling unstable overhead, A realistic accessing scheme for SU payload trans- mission needs to incorporate some SU coordination We are currently working on a distributed Multi-carrier accessing scheme based on Request to Send/Clear to Send messaging, “Best of both worlds”.
Outline 1) Cognitive Radio → Definition → What has already been proposed? 2) Physical Layer Solutions → OFDM → Filter bank methods 3) Channel Sensing 4) Payload Transmission, two corner stones: → Complete central control scheme → Fully Random scheme → Current work: best of both worlds 5) Conclusion
Conclusions: Physical Layer • OFDM as proposed by Weiss et al. suffers from leakage problems which leads to inefficient usage of available spectrum. Up to 100 % overhead. • The Multitaper sensing method as proposed by Haykin does not have leakage problem, but is complicated to implement. • We proposed filter banks as an efficient way for spectral analysis as well as payload transmission. Like the mutitapper, filterbanks do not suffer from leakage. No overhead
Conclusions: Payload Transmission • a fully random accessing scheme will not deliver good system performance: unstable • full central base station control may add a lot of signaling overhead. • Current work focuses on a multi-carrier RTS/CTS based accessing scheme for cognitive radios • We currently investigate in the cross-layer aspects of Filter bank based cognitive radio systems
The End Questions are welcome!
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