Jarmo Lundén

Collaborative Cyclostationary Spectrum Sensing for Cognitive Radio Systems

This paper proposes an energy efficient collaborative cyclostationary spectrum sensing approach for cognitive radio systems. An existing statistical hypothesis test for the presence of cyclostationarity is extended to multiple cyclic frequencies and its asymptotic distributions are established. Collaborative test statistics are proposed for the fusion of local test statistics of the secondary users, and a censoring technique in which only informative test statistics are transmitted to the fusion center (FC) during the collaborative detection is further proposed for improving energy efficiency in mobile applications. Moreover, a technique for numerical approximation of the asymptotic distribution of the censored FC test statistic is proposed. The proposed tests are nonparametric in the sense that no assumptions on data or noise distributions are required. In addition, the tests allow dichotomizing between the desired signal and interference. Simulation experiments are provided that show the benefits of the proposed cyclostationary approach compared to energy detection, the importance of collaboration among spatially displaced secondary users for overcoming shadowing and fading effects, as well as the reliable performance of the proposed algorithms even in very low signal-to-noise ratio (SNR) regimes and under strict communication rate constraints for collaboration overhead.

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.

Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio

Cognitive radios sense the radio spectrum in order to find underutilized spectrum and then exploit it in an agile manner. Spectrum sensing has to be performed reliably in challenging propagation environments characterized by shadowing and fading effects as well as heavy-tailed noise distributions. In this paper, a robust computationally efficient nonparametric cyclic correlation estimator based on the multivariate (spatial) sign function is proposed. Nonparametric statistics provide additional robustness against heavy-tailed noise and when the noise statistics are not fully known. Asymptotic distribution of the spatial sign cyclic correlation estimator under the null hypothesis is established. Tests using constraint on false alarm rate are derived based on the estimated spatial sign cyclic correlation for single-user and collaborative spectrum sensing by multiple secondary users. Theoretical justification for detecting cyclostationary signals using the spatial sign cyclic correlation is provided. A sequential detection scheme for reducing the average detection time is proposed. Simulation experiments and theoretical results comparing the proposed method with cyclostationary spectrum sensing methods employing the conventional cyclic correlation estimator are presented. Simulations demonstrate the reliable and highly robust performance of the proposed nonparametric spectrum sensing method in both Gaussian and non-Gaussian noise environments.

Automatic Radar Waveform Recognition

In this paper, a system for automatically recognizing radar waveforms is introduced. This type of techniques are needed in various spectrum management, surveillance and cognitive radio or radar applications. The intercepted radar signal is classified to eight classes based on the pulse compression waveform: linear frequency modulation (LFM), discrete frequency codes (Costas codes), binary phase, and Frank, P1, P2, P3, and P4 polyphase codes. The classification system is a supervised classification system that is based on features extracted from the intercepted radar signal. A large set of potential features are presented. New features based on Wigner and Choi-Williams time-frequency distributions are proposed. The feature set is pruned by discarding redundant features using an information theoretic feature selection algorithm. The performance of the classification system is analyzed using extensive simulations. Simulation results show that the classification system achieves overall correct classification rate of 98% at signal-to-noise ratio (SNR) of 6 dB on data similar to the training data

Censoring for Collaborative Spectrum Sensing in Cognitive Radios

Cooperative spectrum sensing among multiple cognitive radios mitigates the effects of shadowing and fading. However, it also generates overhead traffic which consumes more power in battery operated mobile terminals. In this paper a censoring scheme for spectrum sensing is proposed. Only informative test statistics are transmitted to the fusion center or shared with other secondary users. Two cooperative censoring test statistics based on cyclostationarity are proposed. Constant false alarm rate tests are derived and asymptotic distributions of test statistics established. The asymptotic distributions are approximated using characteristic functions. Limits for the censoring (no-send) region are derived. The performance of the proposed censoring scheme is illustrated through simulations in a multipath radio environment. Only a minor performance loss is experienced in comparison to uncensored cooperative detection even under very strict constaints on communication rates for the secondary users.

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.

Spectrum Exploration and Exploitation for Cognitive Radio: Recent Advances

The lack of availability of radio spectrum for wireless communication purposes is becoming a serious problem as more wireless systems and services are being developed and operate in crowded spectral bands. The scarcity of useful radio spectrum is mainly due to the static allocation and rigid regulation of the spectrum use rather than the spectrum being actually fully in use. Flexible spectrum use and cognitive radio technologies provide an approach to alleviating this problem by allowing for secondary spectrum use while the spectrum is underutilized by its primary licensed users. Idle spectrum is a time-frequency-location varying resource. It is a resource that also depends on the relative locations of the primary and secondary receivers and transmitters as well as the instantaneous propagation conditions. By acquiring awareness about the current radio environment and the other spectrum users, cognitive radios can more efficiently exploit idle spectrum and manage interference. Doing so requires a means to explore the spectrum to identify high-quality and persistent local spectral resources and access and share them among a number of users while strictly controlling the interference caused to others, in particular, licensed primary users (PUs). Situational awareness about the state of the spectrum allows for optimal exploitation of underutilized spectrum. For example, idle subbands may be allocated, and waveform parameters may be chosen to maximize the sum-rate for the cognitive users while making sure no harmful interference is caused to the other users of the spectrum.

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.

Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks

This paper introduces a machine learning based collaborative multi-band spectrum sensing policy for cognitive radios. The proposed sensing policy guides secondary users to focus the search of unused radio spectrum to those frequencies that persistently provide them high data rate. The proposed policy is based on machine learning, which makes it adaptive with the temporally and spatially varying radio spectrum. Furthermore, there is no need for dynamic modeling of the primary activity since it is implicitly learned over time. Energy efficiency is achieved by minimizing the number of assigned sensors per each subband under a constraint on miss detection probability. It is important to control the missed detections because they cause collisions with primary transmissions and lead to retransmissions at both the primary and secondary user. Simulations show that the proposed machine learning based sensing policy improves the overall throughput of the secondary network and improves the energy efficiency while controlling the miss detection probability.

Nonparametric Cyclic Correlation Based Detection for Cognitive Radio Systems

In this paper a nonparametric cyclic correlation estimator based on complex generalization of sign function is proposed. Theoretical justification for detecting cyclostationary signals is provided. Asymptotic distribution of the estimator under null hypothesis is established. Constant false alarm rate (CFAR) tests based on estimated sign cyclic correlation are derived for single-user and collaborative spectrum sensing. Simulation experiments comparing the proposed method with cyclostationarity based spectrum sensing methods employing the classical cyclic correlation estimator are performed. Nonparametric statistics provide additional robustness when noise statistics are non-Gaussian or not fully known. Simulations demonstrate the reliable performance and robustness of the proposed non-parametric spectrum sensing method in both Gaussian and non-Gaussian noise environments.