Cluster-based cooperative subcarrier sensing using antenna diversity-based weighted data fusion

Cooperative spectrum sensing (CSS) is used in cognitive radio (CR) networks to improve the spectrum sensing performance in shadow fading environments. Moreover, clustering in CR networks is used to reduce reporting time and bandwidth overhead during CSS. Thus, cluster-based cooperative spectrum sensing (CBCSS) has manifested satisfactory spectrum sensing results in harsh environments under processing constraints. On the other hand, the antenna diversity of multiple input multiple output CR systems can be exploited to further improve the spectrum sensing performance. This paper presents the CBCSS performance in a CR network which is comprised of single- as well as multiple-antenna CR systems. We give theoretical analysis of CBCSS for orthogonal frequency division multiplexing signal sensing and propose a novel fusion scheme at the fusion center which takes into account the receiver antenna diversity of the CRs present in the network. We introduce the concept of weighted data fusion in which the sensing results of different CRs are weighted proportional to the number of receiving antennas they are equipped with. Thus, the receiver diversity is used to the advantage of improving spectrum sensing performance in a CR cluster. Simulation results show that the proposed scheme outperforms the conventional CBCSS scheme

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Location privacy preservation in secure crowdsourcing-based cooperative spectrum sensing

Spectrum sensing is one of the most essential components of cognitive radio since it detects whether the spectrum is available or not. However, spectrum sensing accuracy is often degraded due to path loss, interference, and shadowing. Cooperative spectrum sensing (CSS) is one of the proposed solutions to overcome these challenges. It is a key function for dynamic spectrum access that can increase largely the reliability in cognitive radio networks. In fact, several users cooperate to detect the availability of a wireless channel by exploiting spatial diversity. However, cooperative sensing is also facing some series of security threats. In this paper, we focus on two major problems. The first problem is the localization preservation of the secondary users. In fact, malicious users can exploit spatial diversity to localize a secondary user by linking his location-dependent sensing report to his physical position. The existing solutions present a high level of complexity which decreases the performance of the systems. The second problem is the data injection attack, in which malicious CR users may affect the decisions taken by the cognitive users by providing false information, introducing spectrum sensing data falsification (SSDF). In fact, they can submit false sensing reports containing power measurements much larger (or smaller) than the true value to inflate (or deflate) the final average, in which case the fusion center may falsely determine that the channel is busy (or vacant) which increases the false alarm and miss detection probabilities. In this paper, we propose a novel scheme to overcome these problems: iterative per cluster malicious detection (IPCMD). It utilizes applied cryptographic techniques to allow the fusion center (FC) to securely obtain the aggregated result from various secondary users without learning each individual report. IPCMD combines the aggregated sensing reports with their reputation scores during data fusion. The proposed scheme is based on a new algorithm for key generation which can significantly reduce the key management complexity and consequently increase the system performance. Therefore, it can enable secure cooperative spectrum sensing and improve the secondary user location privacy.Ooreedoo, Doha, QatarScopu

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing.  The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing.  This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties.  The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing

Spectrum sensing, spectrum monitoring, and security in cognitive radios

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. In this dissertation, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user’s signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. In-band spectrum sensing in overlay cognitive radio networks requires that the secondary users (SU) periodically suspend their communication in order to determine whether the primary user (PU) has started to utilize the channel. In contrast, in spectrum monitoring the SU can detect the emergence of the PU from its own receiver statistics such as receiver error count (REC). We investigate the problem of spectrum monitoring in the presence of fading where the SU employs diversity combining to mitigate the channel fading effects. We show that a decision statistic based on the REC alone does not provide a good performance. Next we introduce new decision statistics based on the REC and the combiner coefficients. It is shown that the new decision statistic achieves significant improvement in the case of maximal ratio combining (MRC). Next we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving radios. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. We also consider the problem of centralized binary hypothesis testing in a cognitive radio network (CRN) consisting of multiple classes of cognitive radios, where the cognitive radios are classified according to the probability density function (PDF) of their received data (at the FC) under each hypotheses

A weighted hard combination scheme for cooperative spectrum sensing in cognitive radio sensor networks

Multi-user spatial sensing diversity exploration through cooperation spectrum sensing greatly improves sensing performance. However, high communication overhead and energy costs for exchanging sensing results may limit its viability in a realistic large scale resource constraint network such as cognitive radio wireless sensor networks. This paper presents a Weighted Hard Combination (WHC) scheme that combines features of both quantized and hard combining schemes to minimize energy cost for reporting sensing result and improve primary user detection performance in cooperative sensing. We evaluate the effectiveness of the scheme through simulation. Performance comparison of the WHC scheme in terms of detection performance, reporting energy cost and reporting time ratio with conventional hard combination, soft combination and quantized schemes indicates viability of the scheme. The results indicate that the WHC scheme minimizes reporting energy cost by 70% and improves detection performance by 5.6% compared to the quantized 3-bits scheme

Cooperative subcarrier sensing using antenna diversity based weighted virtual sub clustering

The idea of cooperation and the clustering amongst cognitive radios (CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing (SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing (CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing (OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM (NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining (AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed