Spectrum sensing is one of the enabling functionalities for cognitive radio (CR) systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the CR is required to detect incumbent signals at very low signal-to-noise ratio (SNR). In this paper, we present a spectrum sensing technique based on correlating spectra for detection of television (TV) broadcasting signals. The basic strategy is to correlate the periodogram of the received signal with the a priori known spectral features of the primary signal. We show that according to the Neyman-Pearson criterion, this spectral correlation-based sensing technique is asymptotically optimal at very low SNR and with a large sensing time. From the system design perspective, we analyze the effect of the spectral features on the spectrum sensing performance. Through the optimization analysis, we obtain useful insights on how to choose effective spectral features to achieve reliable sensing. Simulation results show that the proposed sensing technique can reliably detect analog and digital TV signals at SNR as low as -20 dB.
In this paper, we consider cognitive beamforming for multiple secondary data streams subject to individual signal-to-noise ratio (SNR) requirements for each secondary data stream. In such a cognitive radio system, the secondary user is permitted to use the spectrum allocated to the primary user as long as the caused interference at the primary receiver is tolerable. With both secondary SNR constraint and primary interference power constraint, we aim to minimize the secondary transmit power consumption. By exploiting the individual SNR requirements, we formulate this cognitive beamforming probl…
Spectrum sensing is essential in cognitive radio to enable dynamic spectrum access. In many scenarios, primary user signal must be detected reliably in low signal-to-noise ratio (SNR) regime under required sensing time. We propose to use stochastic resonance, a nonlinear filter having certain resonance frequency, to detect primary users when the SNR is very low. Both block and sequential detection schemes are studied. Simulation results show that, under the required false alarm rate, both detection probability and average detection delay can be substantially improved. A few implementation issu…
Compressed sensing (CS) is a sampling paradigm that allows to simultaneously measure and compress signals that are sparse or compressible in some domain. The choice of a sensing matrix that carries out the measurement has a defining impact on the system performance and it is often advocated to draw its elements randomly. It has been noted that in the presence of input (signal) noise, the application of the sensing matrix causes SNR degradation due to the noise folding effect. In fact, it might also result in the variations of the output SNR in compressive measurements over the support of the i…
In this chapter, we present the state of the art of the spectrum sensing techniques for cognitive radio networks as well and their comparisons. The rest of the chapter is organized as below: Section I.1, Section I.2, and Section I.3 present the spectrum management problem and the cognitive radio cycle as well as the compressive sensing solution; Section II.1 describes the spectrum sensing model; Section II.2 presents the existing spectrum sensing techniques, including energy, autocorrelation, Euclidian distance, wavelet, and matched filter based sensing. Finally, a conclusion is given at the e…
Various spectrum sensing approaches have been shown to suffer from a so-called SNR-wall, an SNR value below which a detector cannot perform robustly no matter how many observations are used. Up to now, the eigenvalue-based maximum-minimum-eigenvalue (MME) detector has been a notable exception. For instance, the model uncertainty of imperfect knowledge of the receiver noise power, which is known to be responsible for the energy detector's fundamental limits, does not adversely affect the MME detector's performance. While additive white Gaussian noise (AWGN) is a standard assumption in wireless …
In this paper we address the problem of finding the sensing capacity of sensor networks for a class of linear observation models and a fixed SNR regime. Sensing capacity is defined as the maximum number of signal dimensions reliably identified per sensor observation. In this context sparsity of the phenomena is a key feature that determines sensing capacity. Precluding the SNR of the environment the effect of sparsity on the number of measurements required for accurate reconstruction of a sparse phenomena has been widely dealt with under compressed sensing. Nevertheless the development there w…
Sampling rate is the bottleneck for spectrum sensing over multi-GHz bandwidth. Recent progress in compressed sensing (CS) initialized several sub-Nyquist rate approaches to overcome the problem. However, efforts to design CS reconstruction algorithms for wideband spectrum sensing are very limited. It is possible to further reduce the sampling rate requirement and improve reconstruction performance via algorithms considering prior knowledge of cognitive radio spectrum usages. In this paper, we group the usages of cognitive radio spectrum into three categories and propose a modified orthogonal m…
Herein, we present a detailed analysis of an eigenvalue based sensing technique in the presence of correlated noise in the context of a Cognitive Radio (CR). We use a Standard Condition Number (SCN) based decision statistic based on asymptotic Random Matrix Theory (RMT) for decision process. Firstly, the effect of noise correlation on eigenvalue based Spectrum Sensing (SS) is studied analytically under both the noise only and the signal plus noise hypotheses. Secondly, new bounds for the SCN are proposed for achieving improved sensing in correlated noise scenarios. Thirdly, the performance of …
Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization in next generation cellular networks. A crucial requirement for future cognitive radio networks is wideband spectrum sensing: secondary users reliably detect spectral opportunities across a wide frequency range. In this article, various wideband spectrum sensing algorithms are presented, together with a discussion of the pros and cons of each algorithm and the challenging issues. Special attention is paid to the use of sub-Nyquist techniques, including compressive sensing and multi-chan…
Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and opportunistically use under-utilized frequency bands without causing harmful interference to primary networks. Since individual cognitive radios might not be able to reliably detect weak primary signals due to channel fading/shadowing, this paper proposes a cooperative wideband spectrum sensing scheme, referred to as spatial-spectral joint detection, which is based on a linear combination of the local statistics from spatially distributed multiple cognitive radios. The cooperative sensing …
Wideband spectrum sensing is a critical component of a functioning cognitive radio system. Its major challenge is the too high sampling rate requirement. Compressive sensing (CS) promises to be able to deal with it. Nearly all the current CS based compressive wideband spectrum sensing methods exploit only the frequency sparsity to perform. Motivated by the achievement of a fast and robust detection of the wideband spectrum change, total variation mnimization is incorporated to exploit the temporal and frequency structure information to enhance the sparse level. As a sparser vector is obtained,…
Spectrum sensing is an essential enabling functionality for cognitive radio networks to detect spectrum holes and opportunistically use the under-utilized frequency bands without causing harmful interference to legacy networks. This paper introduces a novel wideband spectrum sensing technique, called multiband joint detection, which jointly detects the signal energy levels over multiple frequency bands rather than consider one band at a time. The proposed strategy is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. The spectrum sensing pro…
In cognitive radio network the primary channel information is beneficial. But it can not be obtained by direct channel estimation in cognitive system as pervious methods. And only one possible way is the primary receiver broadcasts the primary channel information to the cognitive users, but it would require the modification of the primary receiver and additional precious spectrum resource. Cooperative communication is also a promising technique. And this paper introduces an indirect channel sensing method for the primary channel in cognitive amplify-and-forward (AF) relay network. As the signa…
In this paper, energy efficient power adaptation is considered in sensing-based spectrum sharing cognitive radio systems in which secondary users first perform channel sensing and then initiate data transmission with two power levels based on the sensing decisions (e.g., idle or busy). It is assumed that spectrum sensing is performed by the cognitive secondary users, albeit with possible errors. In this setting, the optimization problem of maximizing the energy efficiency (EE) subject to peak/average transmission power constraints and average interference constraints is considered. The circuit…
Multi-rate asynchronous sub-Nyquist sampling (MASS) is proposed for wideband spectrum sensing. Corresponding spectral recovery conditions are derived and the probability of successful recovery is given. Compared to previous approaches, MASS offers lower sampling rate, and is an attractive approach for cognitive radio networks.
This paper considers the generalized cognitive radio channel where the secondary user is allowed to reuse the frequency during both the idle and active periods of the primary user, as long as the primary rate remains the same. In this setting, the optimal power allocation policy with single-input single-output (SISO) primary and secondary channels is explored. Interestingly, the offered gain resulting from the frequency reuse during the active periods of the spectrum is shown to disappear in both the low and high signal-to-noise ratio (SNR) regimes. We then argue that this drawback in the high…
High signal to noise ratio (SNR) consistency of model selection criteria in linear regression models has attracted a lot of attention recently. However, most of the existing literature on high SNR consistency deals with model order selection. Further, the limited literature available on the high SNR consistency of subset selection procedures (SSPs) is applicable to linear regression with full rank measurement matrices only. Hence, the performance of SSPs used in underdetermined linear models (a.k.a compressive sensing (CS) algorithms) at high SNR is largely unknown. This paper fills this gap b…
In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, we apply recent matrix completion techniques to greatly reduce the sensing information needed. We formulate the collaborative…
In this paper, a joint spectrum sensing and accessing optimization framework for a multiuser cognitive network is proposed to significantly improve spectrum efficiency. For such a cognitive network, there are two important and limited resources that should be distributed in a comprehensive manner, namely feedback bits and time duration. First, regarding the feedback bits, there are two components: sensing component (used to convey various users' sensing results) and accessing component (used to feedback channel state information). A large sensing component can support more users to perform coo…
The cognitive radio wireless sensor networks have become an integral part of communicating spectrum information to the fusion center, in a cooperative spectrum sensing environment. A group of battery operated sensors or nodes, sensing information about spectrum availability in the radio links, needs an energy efficient strategy to pass on the binary information. A proper routing method through intelligent decision making can be a good solution. In this paper, an energy efficient routing protocol has been introduced and a performance comparison is made with the existing system in terms of its e…
We consider the problem of Spectrum Sensing in Cognitive Radio Systems. We have developed a distributed algorithm that the Secondary users can run to sense the channel cooperatively. It is based on sequential detection algorithms which optimally use the past observations. We use the algorithm on secondary users with energy detectors although it can be used with matched filter and other spectrum sensing algorithms also. The algorithm provides very low detection delays and also consumes little energy. Furthermore it causes low interference to the primary users. We compare this algorithm to sever…
-In cognitive radio networks, spectrum sensing aims to detect the unused spectrum channels in order to use the radio spectrum more efficiently. Various methods have been proposed in the past, such as energy, feature detection, and matched filter. These methods are characterized by a sensing threshold, which plays an important role in the sensing performance. Most of the existing techniques used a static threshold. However, the noise is random, and, thus the threshold should be dynamic. In this paper, we suggest an approach with an estimated and dynamic sensing threshold to increase the efficie…
Spectrum sensing is a key problem in cognitive radio. However, traditional detectors become ineffective when noise uncertainty is severe. It is shown that the entropy of Gauss white noise is constant in the frequency domain, and a robust detector based on the entropy of spectrum amplitude was proposed. In this paper a novel detector is proposed based on the entropy of spectrum power density, and its performance is better than the previous scheme with less computational complexity. Furthermore, to improve the reliability of the detection, a two-stage entropy-based cooperative spectrum sensing s…
This letter presents an adaptive spectrum sensing algorithm that detects wideband spectrum using sub-Nyquist sampling rates. By taking advantage of compressed sensing (CS), the proposed algorithm reconstructs the wideband spectrum from compressed samples. Furthermore, an l2 norm validation approach is proposed that enables cognitive radios (CRs) to automatically terminate the signal acquisition once the current spectral recovery is satisfactory, leading to enhanced CR throughput. Numerical results show that the proposed algorithm can not only shorten the spectrum sensing interval, but also imp…
Cognitive radio and dynamic spectrum access represent a new paradigm shift in more effective use of limited radio spectrum. One core component behind dynamic spectrum access is the sensing of primary user activity in the shared spectrum. Conventional distributed sensing and centralized decision framework involving multiple sensor nodes is proposed to enhance the sensing performance. However, it is difficult to apply the conventional schemes in reality since the overhead in sensing measurement and sensing reporting as well as in sensing report combining limit the number of sensor nodes that can…
In this paper, we investigate the joint spectrum sensing and resource allocation problem to maximize throughput capacity of an OFDM-based cognitive radio link with a cognitive relay. By applying a cognitive relay that uses decode and forward (D&F), we achieve more reliable communications, generating less interference (by needing less transmit power) and more diversity gain. In order to account for imperfections in spectrum sensing, the proposed schemes jointly modify energy detector thresholds and allocates transmit powers to all cognitive radio (CR) subcarriers, while simultaneously assigning…
Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to exploit the sparse nature of the occupation of the primary users. Also, distributed spectrum sensing has been proposed to tackle the wireless channel problems, like node or link failures, rather than the common (centralized approach) for spectrum sensing. In this paper, we propose a distributed spectrum sensing framework based on consensus algorithms where SU nodes exchange their binary decisions to take global decisions without a fusion center to coordinate the sensing process. Each SU will share its decision with its…
Cognitive radio that supports a secondary and opportunistic access to licensed spectrum shows great potential to dramatically improve spectrum utilization. Spectrum sensing performed by secondary users to detect unoccupied spectrum bands, is a key enabling technique for cognitive radio. This paper proposes a truncated sequential spectrum sensing scheme, namely the sequential shifted chi-square test (SSCT). The SSCT has a simple test statistic and does not rely on any deterministic knowledge about primary signals. As figures of merit, the exact false-alarm probability is derived, and the miss-d…
A cognitive radio system has the ability to observe and learn from the environment, adapt to the environmental conditions, and use the radio spectrum more efficiently. It allows secondary users (SUs) to use the primary users (PUs) channels when they are not being utilized. Cognitive radio involves three main processes: spectrum sensing, deciding, and acting. In the spectrum sensing process, the channel occupancy is measured with spectrum sensing techniques in order to detect unused channels. In the deciding process, sensing results are analyzed and decisions are made based on these results. In…
We consider a spectrum sharing communication scenario in which a primary and a secondary users are communicating, simultaneously, with their respective destinations using the same frequency carrier. Both optimal power profile and ergodic capacity are derived for fading channels, under an average transmit power and an instantaneous interference outage constraints. Unlike previous studies, we assume that the secondary user has a noisy version of the cross link and the secondary link Channel State Information (CSI). After deriving the capacity in this case, we provide an ergodic capacity generali…
Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio in mobile communication. Compressed sensing (CS) is introduced to transfer the sampling burden. The standard sparse signal recovery of CS does not consider the distortion in the analogue-to-information converter (AIC). To mitigate performance degeneration casued by the mismatch in least square distortionless constraint which doesn't consider the AIC distortion, we define the sparse signal with the sampling distortion as a bounded additive noise, and An anti-sampling-distortion constraint (ASDC) is deduced…
Cognitive radios (CR) are intelligent radio devices that can sense the radio environment and adapt to changes in the radio environment. Spectrum sensing and spectrum access are the two key CR functions. In this paper, we present a spectrum sensing error aware MAC protocol for a CR network collocated with multiple primary networks. We explicitly consider both types of sensing errors in the CR MAC design, since such errors are inevitable for practical spectrum sensors and more important, such errors could have significant impact on the performance of the CR MAC protocol. Two spectrum sensing pol…
In cognitive radio systems, employing sensing-based spectrum access strategies, secondary users are required to perform channel sensing in order to detect the activities of primary users. In realistic scenarios, channel sensing occurs with possible errors due to miss-detections and false alarms. As another challenge, time-varying fading conditions in the channel between the secondary transmitter and the secondary receiver have to be learned via channel estimation. In this paper, performance of causal channel estimation methods in correlated cognitive radio channels under imperfect channel sens…
In a cognitive radio system the failure of secondary user (SU) transceivers to promptly vacate the channel can introduce significant access-latency for primary or high-priority users (PU). In conventional cognitive radio systems, the backoff latency is exacerbated by frame structures that only allow sensing at periodic intervals. Concurrent transmission and sensing using self-interference suppression has been suggested to improve the performance of cognitive radio systems, allowing decisions to be taken at multiple points within the frame. In this paper, we extend this approach by proposing a …
A novel concept of Joint Source and Channel Sensing (JSCS) is introduced in the context of Cognitive Radio Sensor Networks (CRSN). Every sensor node has two basic tasks: application-oriented source sensing and ambient-oriented channel sensing. The former is to collect the application-specific source information and deliver it to the access point within some limit of distortion, while the latter is to find the vacant channels and provide spectrum access opportunities for the sensed source information. With in-depth exploration, we find that these two tasks are actually interrelated when taking …
Spectrum sensing is a fundamental component in cognitive radio. A major challenge in this area is the requirement of a high sampling rate in the sensing of a wideband signal. In this paper a wideband spectrum sensing model is presented that utilizes a sub-Nyquist sampling scheme to bring substantial savings in terms of the sampling rate. The correlation matrix of a finite number of noisy samples is computed and used by a subspace estimator to detect the occupied and vacant channels of the spectrum. In contrast with common methods, the proposedmethod does not need the knowledge of signal proper…
Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion a…
We develop an efficient algorithm for cooperative spectrum sensing in a relay based cognitive radio network. We consider a stochastic model where data is sent from the Base Station (BS) of the Primary User (PU). The data is relayed by the Secondary Users (SU) to the SU BS. The SU BS has only partial CSI knowledge of the wireless channels. In order to obtain the optimal decision rule based on Likelihood Ratio Test (LRT), the marginal likelihood under each hypothesis needs to be evaluated pointwise. These, however, cannot be obtained analytically due to the intractability of the integrals. Inste…
This paper studies the symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. Two different transmission schemes, namely sensing-based spectrum sharing (SSS) and opportunistic spectrum access (OSA), are considered. In both schemes, secondary users first perform channel sensing, albeit with possible errors. In SSS, depending on the sensing decisions, they adapt the transmission power level and coexist with primary users in the channel. On the other hand, in OSA, secondary users are allowed to transmit only when the primary user activity is…
This paper deals with the challenging problem of spectrum sensing in cognitive radio. We consider a stochastic system model where the the Primary User (PU) transmits a periodic signal over fading channels. The effect of frequency offsets due to oscillator mismatch, and Doppler offset is studied. We show that for this case the Likelihood Ratio Test (LRT) cannot be evaluated poitnwise. We present a novel approach to approximate the marginilisation of the frequency offset using a single point estimate. This is obtained via a low complexity Constrained Adaptive Notch Filter (CANF) to estimate the …
Various primary user (PU) radios have been allocated into fixed frequency bands in the whole spectrum. A cognitive radio network (CRN) should be able to perform the wideband spectrum sensing (WSS) to detect temporarily unoccupied frequency bands. We summarize four occupancy features for the frequency bands. 1. The occupancy is sparse; 2. The frequency band allocation information is fixed and common; 3. There are three categories for the frequency band usages; 4. The occupied frequency bands are common in the CRN. For the first time, we consider all features as the prior knowledge in the compre…
This paper presents optimization issues of energy detection (ED) thresholds in cooperative spectrum sensing (CSS) with regard to general Gaussian noise. Enhanced ED thresholds are proposed to overcome sensitivity of multiple noise uncertainty. Two-steps decision pattern and convex samples thresholds have been put forward under Gaussian noise uncertainty. Through deriving the probability of detection (Pd) and the probability of false alarm (Pf ) for independent and identical distribution (i.i.d.) SUs, we obtain lower total error rate (Qe) with proposed ED thresholds at low signal-to-noise-ratio…
Spectrum sensing is a fundamental and critical issue for opportunistic spectrum access in cognitive radio networks. Among the many spectrum sensing methods, the information theoretic criteria (ITC) based method is a promising blind method which can reliably detect the primary users while requiring little prior information. In this paper, we provide an intensive treatment on the ITC sensing method. To this end, we first introduce a new over-determined channel model constructed by applying multiple antennas or over sampling at the secondary user in order to make the ITC applicable. Then, a simpl…
In this paper, the problem of opportunistic channel sensing and access in cognitive radio networks when the sensing is imperfect and a secondary user has limited traffic to send at a time is investigated. Primary users' statistical information is assumed to be unknown, and therefore, a secondary user needs to learn the information online during channel sensing and access process, which means learning loss, also referred to as regret, is inevitable. In this research, the case when all potential channels can be sensed simultaneously is investigated first. The channel access process is modeled as…
This paper proposes an optimized sensing based channel access framework for the LTE cognitive femto-cells, with an objective of maximizing the femto-cells downlink throughput. Cognitive femto-cells opportunistically transmit on the macro-cell channels when they are free of use. Those free channels are located by means of spectrum sensing using energy detection. Moreover, periodic sensing is adopted to detect any changes of the sensing outcomes. The maximum attainable femto-cell downlink throughput varies with the macro-cell channel occupancy statistics. Therefore, the LTE macro-cell occupancy …
The successful operation of cognitive radio (CR) between CR transmitter and CR receiver (CR link) relies on reliable spectrum sensing. To network CRs requires spectrum sensing at CR transmitter and further information regarding the spectrum availability at CR receiver. Redefining the spectrum sensing along with statistical inference suitable for cognitive radio networks (CRN), we mathematically derive conditions to allow CR transmitter forwarding packets to CR receiver under guaranteed outage probability, and prove that the correlation of localized spectrum availability between a cooperative n…
In the Cognitive Compressive Sensing (CCS) problem, a Cognitive Receiver (CR) seeks to optimize the reward obtained by sensing an underlying $N$ dimensional random vector, by collecting at most $K$ arbitrary projections of it. The $N$ components of the latent vector represent sub-channels states, that change dynamically from "busy" to "idle" and vice versa, as a Markov chain that is biased towards producing sparse vectors. To identify the optimal strategy we formulate the Multi-Armed Bandit Compressive Sensing (MAB-CS) problem, generalizing the popular Cognitive Spectrum Sensing model, in whic…
In this paper, we investigate the energy efficiency of conventional collaborative compressive sensing (CCCS) scheme, focusing on balancing the tradeoff between energy efficiency and detection accuracy in cognitive radio environment. In particular, we derive the achievable throughput, energy consumption and energy efficiency of the CCCS scheme, and formulate an optimization problem to determine the optimal values of parameters which maximize the energy efficiency of the CCCS scheme. The maximization of energy efficiency is proposed as a multi-variable, non-convex optimization problem, and we pr…
Wideband spectrum sensing is becoming increasingly important to cognitive radio (CR) systems for exploiting spectral opportunities. This paper introduces a novel multi-rate sub-Nyquist spectrum sensing (MS3) system that implements cooperative wideband spectrum sensing in a CR network. MS3 can detect the wideband spectrum using partial measurements without reconstructing the full frequency spectrum. Sub-Nyquist sampling rates are adopted in sampling channels for wrapping the frequency spectrum onto itself. This significantly reduces sensing requirements of CR. The effects of sub-Nyquist samplin…
Spectrum sensing is a fundamental component is a cognitive radio. In this paper, we propose new sensing methods based on the eigenvalues of the covariance matrix of signals received at the secondary users. In particular, two sensing algorithms are suggested, one is based on the ratio of the maximum eigenvalue to minimum eigenvalue; the other is based on the ratio of the average eigenvalue to minimum eigenvalue. Using some latest random matrix theories (RMT), we quantify the distributions of these ratios and derive the probabilities of false alarm and probabilities of detection for the proposed…
In this paper, we examine the outage performance of a cognitive relay network, which is comprised of a secondary transmitter (ST), multiple decode-and-forward (DF) relays and a secondary destination (SD). We propose a multi-relay selection scheme for the cognitive relay network, where multiple relays are selected and used to participate in forwarding the secondary transmission from ST to SD. A closed-form expression of the outage probability for the proposed multi-relay selection under imperfect spectrum sensing is derived in Rayleigh fading environments. For comparison purposes, the conventio…
Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of received signal and noise are usually different, they can be used to differentiate the case where the primary user's signal is present from the case where there is only noise. In this paper, spectrum sensing algorithms are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. Two test statistics are then extracted from the sample covariance matrix. A decision on the signal …
This paper considers cooperative spectrum sensing in Cognitive Radios. In our previous work we have developed DualSPRT, a distributed algorithm for cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at the Cognitive Radios as well as at the fusion center. This algorithm works well, but is not optimal. In this paper we propose an improved algorithm- SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We analyse it theoretically. We also modify this algorithm to handle uncertainties in SNR's and fading.
In this paper we propose the capacity optimization over sensing threshold for sensing-based cognitive radio networks. The objective function of the proposed optimization is to maximize the capacity at the secondary user subject to the constraints on the transmit power and the sensing threshold in order to protect the primary user. The defined optimization problem is a convex optimization over the transmit power and the sensing threshold where the concavity on sensing threshold is proved. The problem is solved by using Lagrange duality decomposition method in conjunction with a subgradient iter…
This letter studies a time-slotted multiple-access system with a primary user (PU) and a secondary user (SU) sharing the same channel resource. We propose a novel secondary access protocol which alleviates sensing errors and detects the availability of primary channels with the highest ability of detection. Under the proposed protocol, the SU may access the channel at one of a predefined instants within the time slot each of which associated with a certain access probability that changes based on the sensing outcome. There is also a possibility of accessing the channel at the beginning of the …
This paper studies energy-efficient power allocation schemes for secondary users in sensing-based spectrum sharing cognitive radio systems. It is assumed that secondary users first perform channel sensing possibly with errors and then initiate data transmission with different power levels based on sensing decisions. The circuit power is taken into account in total power consumption. In this setting, the optimization problem is to maximize energy efficiency (EE) subject to peak/average transmission power constraints and peak/average interference constraints. By exploiting quasiconcave property …
