The robustness to uncertainty of noise power is one of main challenges to spectrum sensing technique. Since the occurrence of noise power uncertainty causes the detection performance of spectrum sensing techniques significantly degrade. In this paper, we propose two novel schemes of twostage spectrum sensing for cognitive radio under environment as noise power uncertainty. The two-stage spectrum sensing technique combines two conventional spectrum sensing techniques to perform spectrum sensing by exploiting their individual advantages. The proposed two-stage spectrum sensing scheme exploits the merits of ED, MME and CAV techniques to determine the existence of the primary user. The ED performs spectrum sensing within a short time and offers a reliable detection at high SNRs condition. MME and CAV are robust to noise power uncertainty. Due to the combination of these techniques, the proposed schemes offer much more reliable detection when the uncertainty of noise power occurs. Even though the proposed technique takes the longest time in sensing period among two-stage spectrum sensing techniques, it is worth using this period of time to protect the primary user from harmful interference caused by the secondary user.
In this paper, we propose a new scheme of an adaptive energy detection, multi-slot double constraints adaptive energy detection (MDCAED), with an objective to improve the detection performance of our previous work, double constraints adaptive energy detection (DCAED). MDCAED exploits multiple mini-slot technique, which achieves the diversity reception concept, to increase the ability to distinguish noise from the PU since the effect of diversity reception increases the received SNR. MDCAED performs spectrum sensing by spitting a sensing slot into a multiple mini-slot. Each mini-slot is performed spectrum sensing using DCAED and the final decision is made by using Kof-N rule. The decision threshold is adapted on the SNR of each mini-slot using DCAED. Therefore, by exploiting the multiple mini-slot concept together with DCAED, is reduced while is improved. Although, the detection performance is improved, the average sensing time of MDCAED slightly increases compared to DCAED since the system threshold needs to adapt more than once. Nevertheless, the average sensing time of MDCAED still achieves the spectrum sensing requirement.
Abstract. Brittleness is a well-known problem in expert systems where a conclusion can be made, which human common sense would recognise as impossible e.g. that a male is pregnant. We have extended previous work on prudent expert systems to enable an expert system to recognise when a case is outside its range of experience. We have also used the same technique to detect new patterns of network traffic, suggesting a possible attack. In essence we use Ripple Down Rules to partition a domain, and add new partitions as new situations are identified. Within each supposedly homogeneous partition we use fairly simple statistical techniques to identify anomalous data. The special feature of these statistics is that they are reasonably robust with small amounts of data. This critical situation occurs whenever a new partition is added.
Spectrum sensing is an elementary function in cognitive radio designed to monitor the existence of a primary user (PU). To achieve a high rate of detection, most techniques rely on knowledge of prior spectrum patterns, with a trade-off between high computational complexity and long sensing time. On the other hand, blind techniques ignore pattern matching processes to reduce processing time, but their accuracy degrades greatly at low signal-tonoise ratios. To achieve both a high rate of detection and short sensing time, we propose fast spectrum sensing with coordinate system (FSC) -a novel technique that decomposes a spectrum with high complexity into a new coordinate system of salient features and that uses these features in its PU detection process. Not only is the space of a buffer that is used to store information about a PU reduced, but also the sensing process is fast. The performance of FSC is evaluated according to its accuracy and sensing time against six other well-known conventional techniques through a wireless microphone signal based on the IEEE 802.22 standard. FSC gives the best performance overall.
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