Spectrum Access For Secondary Users Research Articles

Throughput of cognitive radio networks with improved energy detector under security threats

SummarySpectrum sensing in cognitive radio networks (CRNs) is subjected to some security threats such as primary user emulation (PUE) attack and spectrum sensing data falsification (SSDF) attack. In PUE attack, a malicious user (MUPUE) transmits an emulated primary signal throughout the spectrum sensing interval to secondary users (SUs) to forestall them from accessing the primary user (PU) spectrum bands. In SSDF attack, malicious users (MUSSDF) intentionally report false sensing decisions to the fusion center (FC) to influence the overall decision. While most of the existing literatures have studied the effects of these 2 types of attacks separately, the present paper evaluates the secondary network performance in terms of throughput under both the PUE and SSDF attacks with improved energy detectors (IEDs) where SU's spectrum access is hybrid, ie, either in overlay or in underlay mode. An analytical expression on throughput of SU under the simultaneous influence of both of these attacks is developed. Impact of several parameters such as IED parameter, attacker probabilities, and attacker strength on the throughput of SU is investigated. Performance of the present scheme is also compared with only PUE and only SSDF attacks. A simulation test bed is developed in MATLAB to validate our analytical results.

Throughput performance under primary user emulation attack in cognitive radio networks

SummarySpectrum sensing in cognitive radio networks imposes some security threats to the secondary users (SUs) such as primary user emulation attack (PUEA). In the present paper, throughput of an SU is studied for cognitive radio network under PUEA where SU spectrum access is hybrid, ie, either in overlay mode or in underlay mode. A novel analytical expression for throughput of an SU in presence of PUEA is developed. Impact of several parameters such as sensing time, attacker strength, attacker's presence probabilities, maximum allowable SU transmit power, and tolerable interference limit at primary user on the throughput of an SU is investigated. The throughput performance under PUEA and without PUEA is compared.

History-Assisted Energy-Efficient Spectrum Sensing for Infrastructure-Based Cognitive Radio Networks

Spectrum sensing is a prominent functionality to enable dynamic spectrum access (DSA) in cognitive radio (CR) networks. It provides protection to primary users (PUs) from interference and creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce the number of false alarms and missed detections. Continuous sensing for a long time incurs cost in terms of increased energy consumption; thus, spectrum sensing ought to be energy efficient to ensure the prolonged existence of CR devices. This paper focuses on using of history to help achieve energy-efficient spectrum sensing in infrastructure-based CR networks. The scheme employs an iteratively developed history processing database that is used by CRs to make decisions about spectrum sensing, subsequently resulting in reduced spectrum scanning and improved energy efficiency. Two conventional spectrum sensing schemes, i.e., energy detection (ED) and cyclostationary feature detection (CFD), are enriched by history to demonstrate the effectiveness of the proposed scheme. System-level simulations are performed to investigate the sensitivity of the proposed history-based scheme by performing detailed energy consumption analysis for the aforementioned schemes. Results demonstrate that the employment of history ensued in improved energy efficiency due to reduced spectrum scanning. This paper also suggests which spectrum sensing scheme can be the best candidate in a particular scenario by looking into computational complexity before comparative analysis is presented with other states of the art.

Outage-Constrained Sensing Threshold Design for Decentralized Decision-Making in Cognitive Radio Networks

Efficiency in spectrum utility has been a concern in wireless communications for a long time. Cognitive radios have been seen as a solution to occupy the gaps in the licensed spectrum through opportunistic spectrum access and simultaneous spectrum sharing techniques. For this purpose, spectrum sensing has been vital in providing accurate statistical information regarding licensed or primary user (PU) activity on its spectrum. In this paper, we design new sensing thresholds that take into account the outage caused to the PU as a consequence of cognitive or secondary users (SUs) accessing or sharing the said spectrum. With these new thresholds, we can see more protection to the PU from SU spectrum access transmissions based on missed detections, and eliminate most common assumptions made with spectrum sharing systems. Our thresholds also work with a dynamic decision-making algorithm that allows the SUs to use only the statistical sensing information to understand the network dynamics, and determine its transmission opportunities and corresponding power consumption, in a decentralized and uncooperative cross-layer network.

Analysis of secondary user performance in cognitive radio networks with reactive spectrum handoff

Cognitive radio networks use dynamic spectrum access of secondary users (SUs) to deal with the problem of radio spectrum scarcity . In this paper, we investigate the SU performance in cognitive radio networks with reactive-decision spectrum handoff. During transmission, a SU may get interrupted several times due to the arrival of primary (licensed) users. After each interruption in the reactive spectrum handoff, the SU performs spectrum sensing to determine an idle channel for retransmission. We develop two continuous-time Markov chain models with and without an absorbing state to study the impact of system parameters such as sensing time and sensing room size on several SU performance measures. These measures include the mean delay of a SU, the variance of the SU delay, the SU interruption probability, the average number of interruptions that a SU experiences, the probability of a SU getting discarded from the system after an interruption and the SU blocking probability upon arrival.

Analysis of a multi-user cognitive radio network considering primary users return

Multi-user cognitive radio networks have been considered in the literature recently. However, there is no analytical framework which can provide a model in order to derive the system performance metrics. This paper presents an analytical continuous time framework for a multi-user cognitive radio in which secondary users (SUs) communicate on a single common control channel, the most common protocol discussed in the multi-user category. The proposed analysis method is based on the renewal theory. We prove that the spectrum access of SUs with respect to the primary user (PU) traffic behavior forms a renewal process. Furthermore, the corresponding renewal cycle is derived and metrics such as collision probability and interference time due to both sensing error and PU re-occupancy are formulated in the renewal cycle. Thanks to the proposed continuous time analyses, the transmission efficiency for the secondary network is formulated. Finally, some numerical analyses are provided on the derived equations to discuss the optimum transmission time for SUs in order to have the maximum efficiency under the prescribed collision constraint. As an example, for the sensor, probabilities of false alarm and miss-detection are taken to be equal to 0.1 and 0.01, respectively, with a primary network in which the idle and busy cycle rates are 0.1 and 0.3; also, the transmission time must be bounded to 500 ms in order for the collision probability to remain under 0.01. At the end, we show that simulation results are consistent with the numerical analyses.

Optimizing SU Transmission Time Under Collision Constraint Considering PU Returns

ABSTRACTThere are many studies in cognitive radio networks to find the best transmission time for secondary users (SUs) to maximize transmission or energy efficiency. Increasing sensing duration leads to higher sensing accuracy and less interference. However, it decreases SUs' transmission time and increases energy consumptions for spectrum sensing. Conversely, longer transmission duration results in more opportunities for SUs to access the channel and reasonably higher interference with primary user (PU) due to the higher probability of PU returns. This paper presents an analytical framework based on the renewal process for SU spectrum access. Next, the amount of the interference between PU and SU that is due to the PU returns is derived analytically. The probability of the collision and the transmission efficiency are formulated to obtain the optimum transmission time.

Joint optimisation of transmission and waiting times in cognitive radio

Transmission time optimisation is one of the key considerations of cognitive network design. There are many studies in cognitive radio networks (CRNs) focusing on finding the best transmission time for secondary users (SUs) to maximise transmission or energy efficiency. While longer sensing duration leads to a higher sensing accuracy and causes less interference, the SU spends less time for transmission and more energy on sensing spectrum. On the other hand, when the transmission duration becomes longer, although the SU has more opportunities to access the channel, it may encounter higher interference due to primary user (PU) returns and the probability of collision becomes higher. In this article, in a decentralised slotted protocol for CRN, the SU spectrum access is proved as a renewal process, then the interference due to PU return during SU transmission, the missed opportunities due to waiting for the channel to become idle and the energy consumed by the SU in the whole spectrum access process including idling energy, transmission energy and sensing energy consumption are formulated and integrated into newly defined efficiency to obtain the optimum transmission time and waiting time.

On the Usage of History for Energy Efficient Spectrum Sensing

Spectrum sensing is one of the most challenging issues in cognitive radio networks. It provides protection to primary users (PUs) from interference and also creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce number of false alarms and missed detection. At the same time, spectrum sensing should be energy efficient to ensure the longevity of cognitive radio devices. This work presents a novel scheme which investigates the usage of history for energy efficient spectrum sensing in infrastructure cognitive radio networks. The presented scheme employs an iteratively developed history processing database. It is shown that usage of history helps predicting PU activity and results into reduced spectrum scanning by SUs thereby improving the sensing related energy consumption.

A Novel Evolutionary-Based Cooperative Spectrum Sensing Mechanism for Cognitive Radio Networks

Cognitive radio (CR) is considered as a feasible intelligent technology for 4G wireless networks or self-organization networks and envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, one of the main concerns is to reliably sense the presence of primary users, to attain protection against harmful interference caused by the potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, genetic algorithm (GA) and particle swarm optimization (PSO) are investigated. An imperialistic competitive algorithm (ICA) is proposed to minimize error detection at the common soft data fusion (SDF) center for structurally centralized cognitive radio network (CRN). By using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed method is compared with other evolutionary algorithms, as well as other conventional deterministic, such as maximal ratio combining- (MRC-), modified deflection coefficient- (MDC-), normal deflection coefficient- (NDC-) based SDF schemes and OR-rule HDF based. MATLAB simulations confirm the superiority of the ICA-based scheme over the PSO-, GA-based and other conventional schemes in terms of detection performance. In addition, the ICA-based scheme also shows promising convergence and time running performance as compared to other iterative-based schemes. This makes ICA an adequate solution to meet real-time requirements.

Minimizing Sensing Decision Error in Cognitive Radio Networks using Evolutionary Algorithms

Cognitive radio (CR) is envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, the main concern is to reliably sense the presence of primary users (PUs) to attain protection against harmful interference caused by potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize the total sensing decision error at the common soft data fusion (SDF) centre of a structurally-centralized cognitive radio network (CRN). Using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed methods are compared with each other as well as with other conventional deterministic algorithms such as maximal ratio combining (MRC) and equal gain combining (EGC). Computer simulations confirm the superiority of the PSO-based scheme over the GA-based and other conventional MRC and EGC schemes in terms of detection performance. In addition, the PSO-based scheme also shows promising convergence performance as compared to the GA-based scheme. This makes PSO an adequate solution to meet real-time requirements.

Opportunistic Spectrum Access Using Fuzzy Logic for Cognitive Radio Networks

Recent studies and measurements have shown that, with the traditional spectrum access approach, the radio spectrum assigned to primary (licensed) users is vastly underutilized. While many spectrum methods have been proposed to use spectrum effectively, the opportunistic spectrum access has become the most viable approach to achieve near-optimal spectrum utilization by allowing secondary (unlicensed) users to sense and access available spectrum opportunistically. Opportunistic spectrum access approach is enabled by cognitive radios which are able to sense the unused spectrum and adapt their operating characteristics to the real-time environment. However, a naive spectrum access for secondary users can make spectrum utilization inefficient and increase interference to adjacent users. In this paper, we propose a novel approach using Fuzzy Logic System (FLS) to control the spectrum access. Three descriptors are used: spectrum utilization efficiency of the secondary user, its degree of mobility, and its distance to the primary user. The linguistic knowledge of spectrum access based on these three descriptors is obtained from a group of network experts. 27 fuzzy rules are set up based on this linguistic knowledge. The output of the FLS provide the possibility of accessing spectrum band for secondary users and the user with the greatest possibility will be assigned the available spectrum band.