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Wednesday, December 29, 2010

Cognitive Radio Dynamic Spectrum Access: Spectrum Sensing


In order to access the radio spectrum opportunistically (e.g. in shared-use spectrum access model), an unlicensed user must ensure that the spectrum is not occupied by any licensed user. There are three approaches to identifying spectrum opportunities – database registry, beacon signals, and spectrum sensing. In the database registry approach, the information about spectrum opportunity is exchanged between the licensed and unlicensed users through a central database. This information can be synchronized using beacon signals transmitted between licensed and unlicensed users over a common channel. However, both approaches suffer from high infrastructure cost and have limited ability for dynamic spectrum access. On the other hand, the spectrum sensing approach relies only on unlicensed users and requires them to identify and track spectrum opportunities. An unlicensed user transmits when the spectrum is not occupied by the licensed user. As a result, no modification to the existing infrastructure of a licensed system is required. Therefore, dynamic spectrum access through spectrum sensing is compatible with legacy wireless communications systems.
To protect a licensed user from interference, an unlicensed user must periodically sense the spectrum, e.g. for every Tp units of time. If the spectrum is indicated to be idle, the unlicensed user will access the spectrum. However, during the transmission, this unlicensed user will be unaware of the licensed user until the next spectrum sensing is performed. With a single radio transceiver, the spectrum cannot be sensed and accessed simultaneously by the same unlicensed user. Therefore, if a licensed user starts transmission in between two sensing points as shown below, it will be interfered with by the transmission of the unlicensed user. Therefore, the QoS performances of both licensed and unlicensed users will depend on the sensing period Tp. In particular, if Tp is large, the duration of possible interference for a licensed user is large.



For example, if Tp = 10 ms, the longest duration that a licensed user will be interfered with by an unlicensed user will be 10 ms. However, the length of the spectrum sensing period introduces a performance tradeoff. The shorter the sensing period Tp, the shorter will be the interference duration to a licensed user, but the lower will be the throughput for an unlicensed user. For example, with the sensing duration Ts = 1 ms, if Tp = 2 ms, the total transmission time for an unlicensed user in a time interval of 10 ms is only 5 ms. However, if Tp = 10 ms, the transmission time becomes 9 ms. This tradeoff between the QoS performances of the licensed and unlicensed users has to be considered when determining this sensing period Tp. If the transmitter of an unlicensed user is far from the receiver of a licensed user (i.e. primary user), depending on the interference temperature limit at the receiver of a licensed user, both the licensed and the unlicensed users (i.e. secondary users) could transmit their data simultaneously. In this case, the interference range is defined as the minimum distance that an unlicensed transmitter should be away from so that it does not cause “unacceptable” interference to a receiver of a licensed user.





hallenging due to the following reasons:

 Channel uncertainty: Channel uncertainty arises due to dynamic variations in the channel fading and shadowing conditions. Signal power received from a licensed transmitter could be lower than the detection sensitivity due to deep fade when the licensed receiver is in the interference range of an unlicensed user.
 Noise uncertainty: To calculate the detection sensitivity of an unlicensed user, the noise power is required, which is usually not known. The uncertainty in noise power will affect the estimation of detection sensitivity, especially in the case of spectrum sensing through an energy detector, since this type of detection cannot distinguish between the signal from a licensed user and the noise signal. However, spectrum sensing based on feature detection is not severely impacted by this noise uncertainty.
 Aggregated-interference uncertainty: When there are many unlicensed users in the same cognitive radio network, they can interfere with each other. Since the number of unlicensed users and their transmission parameters may not be known, estimation of the interference due to the unlicensed users becomes very challenging. In particular, an unlicensed user may not be able to detect transmissions from a nearby licensed user due to interference caused by transmissions from other unlicensed users. Modeling of this aggregated interference will be useful to characterize the effects of network parameters (e.g. the number, location, transmit power, and propagation detail of users) on the performance of the cognitive radio network

To mitigate the problem of uncertainty in spectrum sensing in a cognitive radio network, cooperative spectrum sensing can be used. Cooperative spectrum sensing provides diversity gain, which improves the detection performance of an unlicensed user. In this case, multiple unlicensed users cooperatively sense the target spectrum and share the spectrum sensing results with each other. One advantage of cooperative spectrum sensing is shown in the Figure below. In this figure, unlicensed user U1 may not be able to detect transmission from licensed user L1 due to channel fading. If U1 starts transmission, it will interfere with data reception at the licensed node L2. However, if unlicensed user U2 senses the spectrum and reports the presence of licensed user L1 to the controller (e.g. over a dedicated control channel), U2 can be notified by this controller and will defer its transmission to avoid any interference to the licensed node L2.


Clearly, cooperative spectrum sensing can be used to combat the noise and channel uncertainties, and, thereby, the probability of mis-detection and false alarm can be decreased. Note that the probability of mis-detection is defined as the probability that an occupied spectrum is sensed to be idle, while the probability of false alarm is the probability that an idle spectrum is sensed to be occupied by a licensed user. Also, cooperative spectrum sensing reduces the sensing time while improving the spectrum sensing accuracy. However, cooperative spectrum sensing incurs higher complexity and overhead (e.g. an increase in energy consumption ).

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