Sachin Chaudhari

Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios

This paper introduces a simple and computationally efficient spectrum sensing scheme for Orthogonal Frequency Division Multiplexing (OFDM) based primary user signal using its autocorrelation coefficient. Further, it is shown that the log likelihood ratio test (LLRT) statistic is the maximum likelihood estimate of the autocorrelation coefficient in the low signal-to-noise ratio (SNR) regime. Performance of the local detector is studied for the additive white Gaussian noise (AWGN) and multipath channels using theoretical analysis. Obtained results are verified in simulation. The performance of the local detector in the face of shadowing is studied by simulations. A sequential detection (SD) scheme where many secondary users cooperate to detect the same primary user is proposed. User cooperation provides diversity gains as well as facilitates using simpler local detectors. The sequential detection reduces the delay and the amount of data needed in identification of the underutilized spectrum. The decision statistics from individual detectors are combined at the fusion center (FC). The statistical properties of the decision statistics are established. The performance of the scheme is studied through theory and validated by simulations. A comparison of the SD scheme with the Neyman-Pearson fixed sample size (FSS) test for the same false alarm and missed detection probabilities is also carried out.

Cooperative Sensing With Imperfect Reporting Channels: Hard Decisions or Soft Decisions?

This paper focuses on the performance analysis and comparison of hard decision (HD) and soft decision (SD) based approaches for cooperative spectrum sensing in the presence of reporting channel errors. For cooperative sensing (CS) in cognitive radio networks, a distributed detection approach with displaced sensors and a fusion center (FC) is employed. For HD based CS, each secondary user (SU) sends a one-bit hard local decision to the FC. For SD based CS, each SU sends a quantized version of a local decision statistic such as the log-likelihood ratio or any suitable sufficient statistic. The decision statistics are sent through channels that may cause errors. The effects of channel errors are incorporated in the analysis through the bit error probability (BEP). For HD based CS, the counting rule or the K-out-of-N rule is used at the FC. For SD based CS, the optimal fusion rule in the presence of reporting channel errors is derived and its distribution is established. A comparison of the two schemes is conducted to show that there is a performance gain in using SD based CS even in the presence of reporting channel errors. In addition, a BEP wall is shown to exist for CS such that if the BEP is above a certain value, then irrespective of the received signal strength corresponding to the primary user, the constraints on false alarm probability and detection probability cannot be met. It is shown that the performance of HD based CS is very sensitive to the BEP wall phenomenon while the SD based CS is more robust in that sense.

Collaborative autocorrelation-based spectrum Sensing of OFDM signals in cognitive radios

A simple and efficient spectrum sensing scheme for orthogonal frequency division multiplexing (OFDM) signals of primary user in cognitive radio systems is proposed in this paper. A detector exploiting the well-known autocorrelation property of cyclic prefix (CP) based OFDM signals is developed. The proposed scheme is then extended to the case of many secondary users collaborating in order to detect the primary user in the face of shadowing and fading. The amount of information each user sends to other users or fusion center is constrained by censoring scheme where only informative decision statistics are sent. Censoring allows reducing the power consumption in battery operated mobile terminals. The statistical properties of the decision statistics are established. Limits on the censoring region are found under constraints on false-alarm and transmission rates. The distribution of the test statistics for cooperative detection with censoring is approximated using characteristic functions. The performance of the scheme is studied by simulations.

Distributed Autocorrelation-Based Sequential Detection of OFDM Signals in Cognitive Radios

This paper addresses the problem of collaborative spectrum sensing using sequential detection (SD) in cognitive radios. The goal of sequential processing is to reduce the delay and amount of data needed in identifying underutilized spectrum. Each secondary user (SU) employs a simple and computationally efficient autocorrelation-based detector for orthogonal frequency division multiplexing (OFDM) signals of the primary user (PU). The decision statistics from individual detectors are combined in a fusion center that may be a separate node or one of the secondary users. The statistical properties of the decision statistics are established. The performance of the scheme is studied by theory and simulations. A comparison of the SD scheme with the Neyman-Pearson fixed sample size (FSS) test for the same false alarm and missed detection probabilities is also carried out.

BEP walls for collaborative spectrum sensing

The main focus of this paper is to present a performance limitation of collaborative spectrum sensing in cognitive radios with imperfect reporting channels. We consider hard decision (HD) based cooperative sensing (CS), in which each SU sends a one-bit binary decision corresponding to the absence or the presence of primary user (PU) to a fusion center (FC). Each SU sends the hard decision over a reporting channel that may cause bit errors. The effect of reporting channel errors is modeled through the widely used bit error probability (BEP). The FC fuses the local binary decisions from all the SUs to make a final decision. Counting rule or K-out-of-N fusion rule is considered for CS and its performance is studied using analytical tools and simulations. Under the constraints on the error probabilities of false alarm and missed detection, a performance limitation in the form of a BEP wall is shown to exist for the counting rule. If the BEP of the reporting channel is above the BEP wall value, then constraints on the cooperative detection performance cannot be met at the FC irrespective of the received signal quality on the listening channel or the sensing time at the SUs. Expressions for the BEP walls are presented for K-out-of-N fusion rules in terms of the error probabilities at the FC and the number of SUs collaborating. The BEP wall values are shown to be sufficiently low to be of practical importance.

BEP walls for cooperative sensing in cognitive radios using K-out-of-N fusion rules

Cooperative spectrum sensing is used to identify idle spectrum in cognitive radio or co-existing wireless systems. This paper identifies a performance limitation for cooperative sensing (CS) involving distributed secondary users (SUs) and a fusion center (FC). The local binary decisions are fused at the FC using a K-out-of-N fusion rule. The performance limitation presented in this paper for CS is in the form of a bit error probability (BEP) wall and results from imperfect reporting channels between the SUs and the FC. That is, if the BEP of the reporting channel exceeds the BEP wall value, then irrespective of the received signal quality on the listening channel or the sensing time at the SUs, constraints on the detector performance cannot be met at the FC. The BEP wall is an important phenomenon and needs to be taken into account while designing communication protocol between the SUs and the FC. Expressions for the BEP walls are derived in terms of the error probabilities at the FC and the number of users cooperating. Further, important properties of the BEP walls are derived and discussed in detail for the K-out-of-N fusion rules under the assumption of independent and identically distributed reporting channel errors.

Effect of quantization and channel errors on collaborative spectrum sensing

This paper analyzes the effect of quantization and channel errors on the performance of collaborative spectrum sensing in cognitive radios. Each secondary user (SU) employs a simple and computationally efficient autocorrelation-based detector for Orthogonal Frequency Division Multiplexing (OFDM) signals of the primary user (PU). The local decision statistics in the form of log-likelihood ratio (LLR) from individual detectors are quantized and sent to the fusion center (FC). The statistical properties of the decision statistics in the presence of quantization are established. The quantized decision statistics are sent through a channel that may cause errors. The effect of channel errors is incorporated in the analysis through Bit Error Probability (BEP). The detection performance at the fusion center is studied using analytical tools and simulations.

On the implementation of autocorrelation-based feature detector

Emerging wireless systems demand more spectrum in order to provide high data rate services. It is known that most of the licensed frequency bands are underutilized because of the rigid spectrum allocation. Cognitive radios aim to relief the situation by identifying and exploiting the underutilized radio spectrum. A key task of the cognitive radio is spectrum sensing, which finds free spectrum and detects licensed spectrum user transmissions. This paper presents an FPGA implementation of an autocorrelation-based feature detector for OFDM-based primary user signals. The autocorrelation-based detection algorithm is optimized in order to achieve power and area efficient hardware realization. The algorithm is implemented in an FPGA evaluation environment, and the performance is verified with measurements.

Measurement campaign for collaborative sensing using cyclostationary based mobile sensors

Spectrum sensing is a key component for obtaining spectrum awareness required for dynamic spectrum access in cognitive radio networks. It helps in finding spectrum opportunities for the secondary user (SU) while managing the interference to the primary user (PU). The performance of single-user sensing degrades in the presence of multipath fading and shadowing. Cooperative sensing can overcome this issue by providing diversity gains. There are also added benefits of performance improvement and simpler detectors. Although there is lot of literature on cooperative sensing, there is still lack of measurement campaigns verifying the cooperative gains in practical scenarios using mobile sensors. In this paper, the gain of using cooperative sensing is evaluated based on the large-scale measurement campaign carried out in Helsinki, Finland using six cyclostationary based mobile sensors measuring over 100 locations. The measurements are carried out for 16 DVB-T channels with bandwidth of 8 MHz each. Fusion rules such as OR, AND, MAJORITY, and SUM (of cyclostationary based local test statistics) are employed and their performances compared to a cyclostationary based local detector. The results demonstrate the gains obtained by cooperative sensing based on the real-world field measurements using mobile sensors.

Performance limitations for cooperative spectrum sensing with reporting channel errors

The main focus of this paper is on the performance limitations for cooperative spectrum sensing in cognitive radios caused by the reporting channel errors. In this paper, we consider hard decision (HD) based cooperative sensing (CS), where each secondary user (SU) detecting a primary user (PU) sends a one-bit local decision to the fusion center (FC). The reporting channel errors may cause bit errors which may be non-identically distributed. The effect of reporting channel errors is incorporated in the analysis through Bit Error Probability (BEP). The detection performance at the fusion center is studied for the counting rule or K-out-of-N fusion rule using analytical tools and simulations. For CS, a BEP wall exists under the imposed constraints on the false alarm probability and missed detection probability. In this paper, we specifically study the BEP wall phenomenon for the counting rule in a general scenario, where the reporting channels are independent but may or may not be identically distributed.