3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom), 2008. Fuzzy-based Spectrum Handoff in Cognitive Radio Networks Giupponi, L. Centre Tecnol. de Telecomunicacions de Catalunya, Castelldefels (Barcelona), Spain Perez-Neira, A.I. Universitat Politècnica de Catalunya, Barcelona, Spain
Outline • Introduction • Fuzzy Logic • Fuzzy-Based Spectrum Handoff • Results and Discussion • Conclusion
Introduction • a cognitive radio is able • to properly sense the spectrum conditions and, • to increase efficiency in spectrum utilization. • it seeks to underlay, overlay or interweave its signals with those of the licensed users, • without impacting their transmission • we focus on the underlay paradigm, • concurrent transmissions of primary (licensed) users (PUs) and secondary (unlicensed) users (SUs) may occur only ifthe aggregate interference caused by the SUs at the PUs is maintained below some acceptable threshold
Introduction (cont.) • The PUs are not cognitive radio aware. • Spectrum sensing • An SU has to continually monitor the radio spectrum • to identify spectrum opportunities, • to reliably detect the presence of PUs, and • to evaluate the interference the SUs transmitter may cause on the PUs receivers • Spectrum handoff • If a PU is detected and if the SU is causing harmful interference to it, • the frequency channel has to be quickly vacated
Introduction (cont.) • Centralized architecture • IEEE 802.22: Wireless Regional Area Networks (WRAN) • the first worldwide standard based on the cognitive radio technology • a BS manages its own cell and all the associated users • DIMSUMnet  • a centralized network level brokering mechanism is implemented • Decentralized architecture • CORVUS  • is based on local spectrum sensing, where PU detection and spectrum allocation are performed based on the coordinated and collaborative operation of SUs.  M. M. Buddhikot, P. Kolody, S. Miller, K. Ryan, J. Evans, DIMSUMnet: new directions in wireless networking using coordinated dynamic spectrum access, in: Proc. IEEE WoWMoM 2005, June 2005, pp. 78-85.  D. Cabric, S. M. Mishra, D. Wilkomm, R. Brodersen, A. Wolisz, A Cognitive radio approach for usage of virtual unlisenced spectrum, in: Proc. 14th IST Mobile Wireless Communications Summit, June 2005.
Introduction (cont.) • we focus on a decentralized architecture, and • we pay particular attention to the problem of the spectrum handoff decision and • on the way how a SU makes this decision based on the information available from the spectrum sensing. • It is worth mentioning that the same spectrum handoff algorithm proposed could be applied to a centralized architecture.
Introduction (cont.) • PUs and SUs coexistence is characterized by high uncertainty • decentralization make the inputs very vague and imprecise • the activity of the PUs, their transmission parameters or the position of the PU receivers • fading, path loss, interference and noise introduce an intrinsic uncertain component as in any wireless channel • the multiple decision making inputs are heterogeneous and not directly comparable • e.g. power levels, signal to noise ratios (SNR), Quality of Service (QoS) indicators, etc. • the spectrum handoff algorithm has to be characterized by • low complexity and reduced execution time • in order to quickly vacate licensed channels when required
Introduction (cont.) • we propose to use fuzzy logic • to deal with the incompleteness, uncertainty and heterogeneity of a cognitive radio scenario. • an additional advantage is its real-time capability • simple enough to guarantee low complexity implementation and quick spectrum mobility • an initial work and a first contribution to the application of fuzzy logic to cognitive radio networks
Fuzzy Logic • To implement decision making processes, fuzzy logic makes use of the so called Fuzzy Logic Controllers (FLCs). • provides an algorithm which convert the linguistic control strategy based on expert knowledge into an automatic control strategy
Fuzzy-Based Spectrum Handoff • The fuzzy-based spectrum handoff is implemented at the SU. • To avoid interference on PU receivers, the SU should have knowledge of its position, • which is an extremely challenging research problem. • Assume that PUs alternatively act as transmitters or receivers, so that a SU, through proper spectrum sensing, is able to detect information about PUs’ activity, • and consequently to approximate their position
Fuzzy-Based Spectrum Handoff (cont.) estimating the distance between the PU and SU making the spectrum handoff decision SSPU: The signal strength received at the SU from the PU SNRPU: an estimation of the signal to noise ratio at the PU PSU: the power at which the SU should transmit RSU: the bit rate of the SU; an indicator of the QoS perceived by the SU HO: whether the handoff has to be realized or not. MODPSU: what the SU transmission power PSU should be
FLC 1 • SSPU is not enough information to decide the power at which the SU can transmit. • Assume that by means of spectral estimation techniques, the SU is able to qualitatively determine the bit rate of the PU (RPU). • Based on RPU, an estimation of SNRPU can be deduced , and consequently also an estimation of the PU transmission power (PPU).  IEEE 802.11g-2003.  IEEE 802.11a-1999
FLC2 • A spectrum handoff can be initiated under two circumstances: • The QoS of the SU is considered dissatisfactory. e.g., rule 4 • The interference caused by the SU on the PU is considered harmful. e.g., rules 25, 26 and 27. • In order to reduce the spectrum handoff rate, the PSU can be properly modified. e.g., rule 13
Results and Discussion • a single cell scenario has been considered • a random walk model inside the coverage area • mobile speed between 0 and 120 km/h and • a randomly chosen direction • the user’s activity periods are defined according to a Poisson scheme • an average of 6 calls per hour and • an average call duration of 180 seconds • The propagation model considered is given as a function of the distance d by L(dB)=128.1+37.6 log(d(km)) • The fading is introduced with a standard deviation equal to 7 and the correlation distance is 20 mt. • The maximum transmission power of both PU and SU is 20 dBm. • The channel frequency Bc is 200 KHz. • The algorithm is activated every 100 ms. • when PSU is modified, it is reduced/increased of 3 dB • only one couple of PUs, 20/2 couples of SUs
Results and Discussion (cont.) • Percentage of spectrum handoffs • A spectrum handoff is realized when the FLC 2 makes the corresponding decision. • Interference temperature • the temperature equivalent of the RF power available at the PU receiving antenna per unit of bandwidth, measured in units of Kelvin . fc: central frequency Bc: bandwidth NSU: is the number of SUs causing harmful interference to the PU Pi(fc, Bc): the average interference power centred at fc k: Boltzmann’s constant  FCC Notice of Inquiry and Notice of Proposed Rule Making, “In the matter of establishment of an interference temperature metric to quantify and manage interference and to expand available unlicensed operation in certain fixed, mobile and satellite frequency bands,” ET Docket No. 03-237, November 13, 2003.
Results and Discussion (cont.) • fuzzy-based spectrum handoff algorithm is compared to an fixed threshold spectrum handoff algorithm • PSU is randomly selected and • the spectrum handoff is initiated when • SSPU > -92 dBm or • the PSU < 7 dBm or • RSU < 350 Kbit/s.
Conclusion • the problem of making decisions in a cognitive radio network is extremely challenging. • the inputs available are confused and heterogeneous due to the characteristics of the wireless channel. • the decision making process is not supported by a central entity, • the decisions are made based on incomplete, qualitative and vague information available at the SU • the proposed fuzzy-based approach to initiate spectrum handoffs is simple and low computationally complex, • which guarantees real-time operation and quick decisions • Simulation results have shown that the fuzzy solution outperforms a solution based on fixed thresholds • in terms of spectrum handoff rate and • interference temperature measured at the PU receiver
Limitation and Future work • the main weakness of FLCs is the dependability of their decisions on the way how membership functions and fuzzy inference rules are set. • fuzzy logic is often combined with learning algorithm • based on e.g. neural networks or genetic algorithms.
Comments • The issues of Underlay mode • Propose a way to implement low complexity handoff algorithm • Reduce handoff rate • As an initial research using simple scenarios