Algorithms
This section will introduce spectrum sensing in cognitive radio. The different ar- eas of spectrum sensing will be addressed, especially focusing on blind spectrum sensing, which is the area of concentration chosen for the research. In addition, two common blind spectrum detection algorithm are presented. These two sensing algorithms will provide a reference for the two novel approaches that have been developed by the author during the research presented in this thesis.
In order for a cognitive radio to dynamically utilize available spectrum, it must be able to quickly and robustly determine which parts of the relevant spectrum that are available or not. All further processing and decision making performed by the communicating device is based on the results from the initial sensing. It is obvious that spectrum sensing is extremely important for a cognitive radio device to perform satisfactorily. Hence spectrum sensing is a cornerstone of cognitive radio.
A large amount of research effort has been put into the area of spectrum sensing over the past decade. The research can be divided in two main parts:
1. Blind Spectrum Sensing 2. Non-Blind Spectrum Sensing
As the names imply, blind spectrum sensing algorithms make sensing decisions without any prior knowledge, whereas non-blind approaches utilize some form of a priori knowledge about the underlying signals. Typical known signal features can be modulation type, carrier frequency or pulse shape. Two main categories of non- blind spectrum sensing techniques are based on waveform- [51] or cyclostationarity detection [12]. A main category of blind spectrum sensing is based on energy detection [53]. Another main category is based on signal autocorrelation, where there are both blind [23, 33] and non-blind approaches [6]. In addition, there exists a plethora of smaller separate categories, such as for instance subspace based sensing [59] or model selection based sensing [56, 58]. The following tutorial papers provide a thorough treatment of spectrum sensing approaches in addition to important background information [57, 30, 21]. The papers also include an extensive amount of additional references.
Non-blind spectrum sensing has received a lot of attention in spectrum sensing research. This is not surprising as one can obtain optimal detection results with the right signal knowledge. It is for instance elementary knowledge for anyone involved with communications that the matched filter is the optimal receiver for a known pulse shape in an AWGN channel [37]. However, non-blind spectrum sensing narrows the opportunities and applicability due to the need for a priori information of signal features. This to some extent contradicts the original idea of the cognitive radio as an agile and flexible device able to adapt to its environment, since it through utilizing non-blind spectrum sensing, tailored to specific signals, will be limited in terms of operating spectrum bands. It is crucial to limit the scope of this research, and due to the desire to maintain flexibility for the proposed approaches, it has thus been chosen to only focus on blind spectrum sensing techniques.
This section presented the topic spectrum sensing for cognitive radio and ex- plained how spectrum sensing algorithms can be divided in the two groups blind and non-blind. In addition, the section ended by providing motivation for why only blind spectrum sensing is being investigated in this research.
3.2.1
Challenges in Spectrum Sensing
It was previously argued that blind spectrum sensing techniques are the most universal when designing dynamic cognitive radio systems, since the designer not necessarily has knowledge of primary user’s signal features. The lack of a priori signal knowledge obviously is an additional disadvantage for blind spectrum sensing approaches as opposed to non-blind. However, the two additional challenges to be presented are common to both.
Since the cognitive radio autonomously makes decisions to transmit, often in licensed frequency bands, it is essential to prevent the cognitive radio from inter-
fering with other users. This is an important networking and resource allocation challenge, and to solve this challenge, it is essential that accurate spectrum sensing algorithms are utilized. This sort of resource allocation problem is very similar to the one experienced in networks based on Carrier Sense Multiple Access (CSMA) [24]. In these networks, a main problem is what is referred to as the hidden node
problem or hidden terminal problem [24]. It refers to the fact that while two nodes
A and B in a network can both hear node C, they can be hidden to each other. Assume that A decides to transmit to C, it listens for activity, the channel is clear and it starts to transmit. Then while A is transmitting, B also decides to transmit. B listens, and perceives the bandwidth as available since it can not hear A. B starts transmitting to C as well, and a collision occurs. The hidden node problem is pri- marily caused by physical distance (I.e. Node A and B are placed far apart on each side of node C) or by channel effects such as fading and shadowing. To prevent the hidden node problem and similar interference related problems, the spectrum sensing algorithms must be able to detect the presence/absence of signals at very low signal to noise ratios.
The upcoming IEEE standard 802.22, a Wireless Regional Area Network (WRAN) standard employing cognitive radio technology, is a good example of the stringent requirements imposed on spectrum sensing algorithms. The 802.22 standard ex- ploits white spaces in the spectrum licensed for TV transmission to provide long range wireless broadband Internet. It is not fully developed, but the preliminary standard requires the cognitive radio to sense TV transmissions at −116 dBm with a probability of detection PD ≥ 0.9 and probability of false detection PFD ≤ 0.1
[50, 57]. Spectral detection at such a low signal to noise ratio is a very challenging requirement.
The last challenge is computational complexity. A large number of emerging wireless devices where cognitive radio can provide a potential future benefit are hand held. Hand held devices usually operate on batteries and have limited com- putational resources. Hence it is a challenge to develop fast and robust spectrum sensing algorithms with low computational complexity.
Three main challenges for spectrum sensing have been presented. The lack of a priori knowledge of the signal is limited to blind spectrum sensing, while robust performance in low signal to noise ratios and maintaining a low computational complexity are essential to both. The requirement for reliability and accuracy in the low SNR region is emphasized in the research presented in this thesis.