Overview
Cognitive Radio
Multidimensional Spectrum Awareness A Survey of Spectrum Sensing Algorithms for Challenges Cognitive Radio Applications Spectrum Sensing Methods
Cooperative Spectrum Sensing Tevfik Yucek and Huseyin Arslan Some Examples from Current Standards
Conclusion
EE-360 Presentation: Ceyhun Baris Akcay Stanford University
Cognitive Radio Cognitive Radio: Definitions
FCC definition: “A radio or system that senses its Primary User: A user who has higher priority or operational electromagnetic environment and can legacy rights on the usage of a specific part of the dynamically and autonomously adjust its radio spectrum
operating parameters to modify system operation, Secondary User: A user who has a lower priority such as maximize throughput, mitigate interference, and therefore exploits the spectrum in such a way facilitate interoperability, access secondary that it does not cause interference to primary users. markets.” Spectrum Sensing: The task of obtaining awareness about the spectrum usage and existence of primary users in a geographical area.
Multidimensional Spectrum Awareness: Multidimensional Spectrum Awareness: Radio Space Radio Space
There are a number of dimensions that can be Geographical Space considered for CR applications: Frequency Time Code
Angle of Arrival Challenges: Hardware Challenges: Hardware
High resolution analog to digital converters with Single-Radio Architecture Dual-Radio Architecture large dynamic range are required. Specific time-slots are One radio monitors the CR has to scan a potentially large band of frequencies to find an opportunity, antennas and allocated for sensing channel power amplifiers have to be designed accordingly. Simple and costs less Higher cost, complexity
High speed digital signal processors are required Lower spectrum High spectral efficiency for low delays. efficiency Higher accuracy and Lower sensing accuracy power consumption
Challenges: Hidden Primary Users Challenges: Spread Spectrum
Similar to CSMA, a hidden primary user as Frequency Hopping: The narrowband signal is depicted below may cause collisions. dynamically switched in frequency.
Solution: Cooperative sensing Direct Sequence: The signal is spread to a much wider band.
Result: The signal power is spread to a wider band, which makes it difficult to detect users.
Solution: Perfect synchronization and pattern knowledge
Challenges: Sensing Duration & Challenges: Fusing Decisions in Frequency Cooperative Sensing
Secondary users should vacate their frequency For soft decisions, the log-likelihood or likelihood bands as soon as a primary user starts transmitting. ratio tests may be employed.
This requires frequent sensing of the channel. The data may be combined using SC or EGC.
Reliably detecting primary users is a function of the For hard decisions, AND, OR, M-out-of-N techniques sensing duration. may be employed.
Reliability of detection vs. speed of detection Soft decisions outperform hard decisions when the tradeoff. number of users is small Vacating speed vs. underutilization of secondary users tradeoff. Spectrum Sensing Methods: Challenges: Security Energy Detector
Malicious users may try to emulate primary users Most common method due to its low complexity
(PUE), denying access to secondary users at will. Threshold selection is a challenge
A public key encryption may be used for protection Bad performance under low SNR conditions whereby primary users are required to transmit an Assume the received signal is as follows: ݊ ݓ + ݊ ݏ = ݊ encrypted (signature) value. ݕ Secondary users have to demodulate the signals of then the metric to threshold will be: primary users ே ଶ (݊) ݕ = ܯ Cannot be used with analog modulation ୀ
Spectrum Sensing Methods: Spectrum Sensing Methods: Energy Detector Energy Detector
The decision is made between the following states: The PD vs. PF curve is as follows: , ݊ ݕ ݊ = ݓܪ .(݊)ݓ + ݊ ݏ = ݊ ଵ ݕܪ The performance of the method can be quantified by
PF , probability of false alarm and PD , probability of detection. When w and s are modeled as zero-mean normally distributed with variances ߪ௪మ and ߪ௦మ the metric follows a chi-squared distribution with 2N degrees of freedom.
Spectrum Sensing Methods: Spectrum Sensing Methods: Energy Detector Waveform-Based Sensing
The threshold depends on the noise variance. Fixed patterns are usually utilized in wireless systems such as preambles, pilot signals, spreading sequences, Estimation of noise variance is an important issue, etc. slight inaccuracies may degrade performance. In the presence of such a pattern the received signal The noise variance may be estimated from the can be correlated with it for sensing. In this case the autocorrelation of the received signal. decision metric will be: ே כ ܪ ݂݅ , (݊) ݏ(݊)The threshold may be iteratively determined by ܴ݁ ݕ ୀଵ confining PF to confidence intervals. ܯ = ே כ Fading and shadowing have to be considered for ଵܪ ݂݅ , (݊) ݏ(݊)ݓ ܴ݁ realistic scenarios. ୀଵ Spectrum Sensing Methods: Spectrum Sensing Methods: Cyclostationarity-Based Sensing Radio Identification Based Sensing
Cyclostationary features are caused because of By identifying the transmission technology, complete periodicities in the signal or can be intentionally knowledge about the spectrum characteristics can introduced. be obtained.
Instead of the power spectral density, cyclic spectral In some cases the CR may want to communicate with density(CSD) is used. Since noise is assumed to be the primary system.
WSS, and the signal is cyclic it can be easily Detected energy, channel bandwidth and shape, detected. cyclic frequencies, spectral correlation density, etc. ଶగఈି כ .ݕ(݊ + ߬)ݕ (݊െ߬݁ ) features may be used for identification ܧ = CAF: ܴ௬ഀ ߬ ஶ ିଶగఛ CSD: ܵ ݂, ߙ = σఛୀିஶ ܴ௬ഀ ߬݁
Spectrum Sensing Methods: Spectrum Sensing Methods: Matched Filtering Comparison
Optimum method if there is complete knowledge of the transmitted signal.
Requires a very short time to reach a given probability of false alarm.
Required number of samples grows as O(1/SNR)
Primary users’ signals have to be demodulated, knowledge of modulation scheme, pulse shaping, etc. needed.
Cooperative Sensing: Cooperative Sensing Centralized Sensing
Can solve problems that arise due to noise A central unit collects all the information from CRs, uncertainty, fading and shadowing. identifies and broadcasts opportunity information.
Decreases false alarm rate significantly. Hard or soft decisions can be preferred.
Can solve the hidden primary node problem. If the number of users is large hard decisions are
Efficient algorithms needed for sharing information preferred so as not to waste bandwidth with opportunity information. Most effective when radios experience independent fading or shadowing Another threshold can be employed to decide on the reliability of the data and censor some of the users Cooperative Sensing: Cooperative Sensing: Distributed Sensing External Sensing
Users again share information on opportunities An external node or network performs all the among each other, but every user decides for sensing instead of the CRs and broadcasts the himself. opportunity information.
No dedicated AP needed to gather all information. Spectrum efficiency is increased and power
Less costly. consumption is reduced since CRs do not waste time or power by sensing the channel. Coordination among users may be a problem. Can efficiently solve the hidden primary user Sharing the final decision instead of sensing information may be employed. problem and uncertainties due to shadowing and fading.
Examples from current Systems: Examples from current Systems: IEEE 802.11k Bluetooth
A noise histogram report is generated based on Adaptive Frequency Hopping (AFH) feature is measurements which display all non 802.11 energy introduced to reduce interference with other 2.4 on a channel. GHz systems.
AP collects the data and uses it to regulate access. AFH identifies the transmissions in the ISM band and
The information is used to balance the load of APs avoids their frequencies. in a network as well. Identification is performed by gathering channel statistics such as BER, received signal strength indicator, etc.
Questions and Discussion