# Narrowband And Wideband Models In Wireless Communication Pdf

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- Wideband signal detection for cognitive radio applications with limited resources
- Channel Models for Wireless Communication Systems
- Wideband signal detection for cognitive radio applications with limited resources

## Wideband signal detection for cognitive radio applications with limited resources

Metrics details. Wideband signals are expected to be used to achieve the required quality of service QoS in the next generation of wireless communications, civil and military radar, and many wireless sensor network WSN scenarios. Wideband signal detection has been identified as one of the most challenging problems in the proliferation of the cognitive radio technology.

Moreover in many applications, spectrum sensing in cognitive radio CR is expected to be performed with limited resources in terms of time, computation, and complexity. This paper is dedicated to the detection of a wideband signal with small sample size. However, the limited number of channel observations brings a reduction of confidence in the decision.

A set of new basic probability assignments associated with the hypothesis of the occupied or vacant channel are then proposed to perform the Dempster-Shafer D-S decision process. Simulation results show that the proposed method has much higher sensitivity to sense an occupied channel than the traditional energy detection method ED and the decision fusion method when small sample size is used.

With the evolution and development of various wireless technologies, spectrum resources are becoming scarce due to the increasing need for spectral bandwidth and number of users. Cognitive radio CR technology has attracted a lot of interest, especially for the next generation of wireless communications, many types of radar systems and wireless sensor network WSN [ 1 — 4 ].

In all those systems, wideband signals are expected to be used to achieve the required quality of service QoS. Therefore, wideband signal detection plays an important role in a wide range of wireless communication systems and has been identified as one of the most challenging problems in the CR technology applications [ 5 — 7 ]. Although, there are numerous current research works focusing on wideband signal detection, many severe challenges still exist [ 8 , 9 ].

First of all, in realistic scenarios, it is very difficult to know the number of antennas, the coding scheme, and the structure of the detected signal. Therefore, an accurate blind spectrum sensing method without any prior information is of great interest. Moreover, in order to avoid unexpected harmful interference, CR user must be able to quickly vacate the frequency band when the licensed user starts transmitting. Thus, the sensing time must be limited to an acceptable level, while still guarantee a sufficiently low detection error probability.

For this purpose, a number of spectrum sensing methods have been proposed and investigated in [ 10 — 17 ]. Under no prior knowledge about the wideband signal, energy detection ED has been shown to be the most popular technique in cooperative sensing thanks to its low computational power requirements on wireless devices [ 3 , 13 ]. However, energy detection is limited by the signal-to-noise ratio SNR wall and has high probability of false alarm [ 14 ].

In order to overcome these shortcomings, eigenvalue-based spectrum sensing methods have been proposed [ 14 , 15 ], which are mainly based on the asymptotic or limiting distribution of extreme eigenvalues in order to overcome the noise uncertainty problem. Unfortunately, they cannot be extended to a more general dimensional setting due to their daunting computational cost. Moreover, these techniques require large number of samples, which is often not suitable for real application scenarios [ 16 ].

Thus [ 18 — 20 ], study the spectrum sensing method using goodness-of-fit GoF test for small sample size, relying on the Anderson-Darling AD statistic.

In that case, the GoF test is only performed to assess the rejection or not of the null hypothesis i. Different from the GoF test mentioned above, both hypotheses of the presence and absence of the wideband signal would be considered in the proposed method in order to make full use of the statical information of the binary hypotheses and improve the detection performance. Considering the challenges mentioned above, a robust spectrum sensing method with small sample size is proposed in this work.

On the one hand, this means short time in real-time data processing. Especially when the detecting devices have only a single-radio architecture, the time of sampling and observing the channel is expected to be as short as possible. On the other hand, we consider that only less steady state reception can be obtained in some complex information environment.

However, due to the small number of samples, the estimation performance inevitably suffers from lack of reliability. In order to improve the reliability, Dempster-Shafer D-S theory of evidence [ 22 — 30 ] is used to make a final decision. The main contribution stands in the proposition of two new BPA functions to evaluate the credibility of the collected small samples from a wideband signal and the combination of BPA functions in order to make a more reliable decision.

Moreover, in the proposed scheme, in order to fully exploit the collected samples, both hypotheses of presence or absence of wideband signal are used. Simulations show that the proposed method has much higher sensitivity to detect the presence of a signal than ED- and GoF-based methods.

The rest of the paper is organized as follows. In Section 2 , some spectrum sensing preliminaries are presented. The proposed spectrum sensing scheme including the statistical model of the received small samples, basic probability assignment functions and D-S fusion, is described in Section 3.

Simulation results and conclusions are given in Sections 4 and 5 , respectively. In this paper, we assume that a wideband signal needs to be detected. According to [ 32 ], a signal having a fractional bandwidth greater than 0. Consider that the observed bandwidth is subdivided into K subbands with equal bandwidth B sub. In each subband, signals are band-passed and downconverted to the baseband. In order to provide a detection in a very short time, a limited number of real-valued samples Q are collected in each subband.

In each subband, the signal is oversampled with a factor N , which means that the sampling frequency is very much larger than the subband width. Actually, when N is large enough while maintaining a small Q number of samples , the scheme in each subband can be seen as a narrowband signal sampling process. As the over sampling factor N is increased, the observation duration Q T s is reduced. Wideband signal detection can be formulated as a binary hypothesis problem as follows. In each of the K subbands, Q samples are collected with oversampling factor N.

The more K is large, the more the narrowband signal hypothesis in each subband is true. The more K and N are large, the more the constant signal assumption over Q samples tends to be true. Since the number of samples Q is small, the observation duration is very short and during this short period, the narrowband signal can be approximated as constant. Moreover, in practice, the distribution of the power spectral density of the signal is unknown, we assume that the signal uniformly occupies the full bandwidth which is the most reasonable, fair, and neutral assumption.

As an example, this assumption holds in many multicarrier signals schemes. It allows to model the signal as a constant in both time and frequency domains. In the simulations section below, the values of K , N , and Q have been selected arbitrarily as a matter of example, and some simulations are provided in Subsection 4. In this case, the spectrum sensing problem is equivalent to a standard scenario with Gaussian distributions having equal variance and different means under each hypothesis.

The proposed spectrum sensing method relies on a fusion processing using D-S theory and a new set of BPA functions. BPA definition and evaluation are the key points of the D-S fusion.

In most applications, it is generally assumed that the number of available samples is sufficiently large in order to correctly estimate the BPAs and perform a reliable fusion. But in this work, we consider that the CR device is very limited in terms of sample size. Hence, K variables one from each subband are obtained as. This also validates that Y k in Eq.

Therefore, we propose to calculate K variables Y k and combine them by D-S theory of evidence for a reliable decision. In addition, in order to estimate the belief of the observed signal in each subband, the cumulative distribution functions CDF of Y k under H 0 and H 1 denoted by F 0 y and F 1 y are applied, which are given in [ 33 ]. Moreover, for the proposed method, as explained in the next section, the wideband signal detection is done by evaluating the reliabilities of both H 0 and H 1 hypotheses, which is a beneficial feature that is not used in the conventional GoF test based methods.

In order to evaluate the credibility of the collected samples in the k th subband, we propose two new BPA functions m k H 0 and m k H 1 for H 0 and H 1 hypotheses in Eqs. Importantly, these BPA functions indicate the credibilities for hypotheses H 0 and H 1 to be true, respectively. For example, a larger value of Y k results in a larger m k H 1 and a smaller m k H 0 , and vice versa, as shown in Fig.

Thus, we can make a decision on the presence or not of the wideband signal by comparing m k H 0 and m k H 1. However, since the number of samples Q is small, Y k has been obtained with a small number. This will cause a big uncertainty and increase the conflict between m k H 0 and m k H 1. Then a third BPA function is defined as follows:.

As shown in Fig. Then, for the same value Y k , F 0 must be greater than or equal to F 1. In order to improve the probability of detection and reduce the influence of the conflict evidence, we make a final reliable decision by fusing all BPA functions obtained from the K groups of samples. In order to improve the reliability of detection, we need to combine the K BPA functions and make a final decision. Then, according to the basic D-S theory of evidence and Eqs.

From Eqs. Finally, based on all K subband observations, the decision is made by comparing the ratio between m H 1 and m H 0 as follows:. Therefore, we have developed a simulation model. The simulation settings and some examples are given in Section 4. Consequently, the pseudo code of the proposed spectrum sensing method with small sample size is given in Algorithm 1. Note that the computational complexity of the proposed method mainly comes from D-S fusion step 10 in Algorithm 1.

Moreover, in the proposed scheme, due to the division of the observed bandwidth into K subbands, a large K increases the number of BPA functions then the computational complexity of the D-S fusion.

In this section, the performance of the proposed method is evaluated with simulations. At first, we compare the proposed method with ED under different sampling numbers. Secondly, we evaluate the detection performance of the proposed method with the same total sampling numbers and different subband and sampling numbers.

Finally, we compare with the methods in [ 19 , 20 , 34 ]. With an oversampling factor N , the sampling rate in each subband is then 2 B sub N. In the first simulation, we examine the performance of the proposed method by comparison with the basic ED method, which calculates the total energy in the full bandwidth as.

As performed in many practical schemes, the noise power can be estimated periodically when no signal is expected in the frequency band of interest. Note that in the comparison, the test statistic in ED method is approximated as Gaussian distributed thanks to the number of the total samples e. As can be seen, with the increase of SNR and for a given value of Q, the probability of detection of the proposed method rises up quickly which is better than the trend of the curves of ED method.

In order to clearly reveal the performance of the proposed method, the receiver operating characteristic ROC curves with different sampling numbers are shown in Fig. It is obvious that the performance of the proposed method and ED is improved with the increase of the number of samples. When the probability of false alarm P fa is 0.

In the second simulation, the detection performance of the proposed method with different Q and K is assessed. Moreover, for the same total number of samples, when the wideband is divided into more groups, a better performance can be obtained.

## Channel Models for Wireless Communication Systems

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## Wideband signal detection for cognitive radio applications with limited resources

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In communications , a system is wideband when the message bandwidth significantly exceeds the coherence bandwidth of the channel. Some communication links have such a high data rate that they are forced to use a wide bandwidth ; other links may have relatively low data rates, but deliberately use a wider bandwidth than "necessary" for that data rate in order to gain other advantages; see spread spectrum. A wideband antenna is one with approximately or exactly the same operating characteristics over a very wide Passband. The term Wideband Audio or also termed HD Voice or Wideband Voice denotes a telephony using a wideband codec , which uses a greater frequency range of the audio spectrum than conventional voiceband telephone calls, resulting in a clearer sound. In some contexts wideband is distinguished from broadband in being broader.

JavaScript is disabled for your browser. Some features of this site may not work without it. Narrowband interference detection and mitigation for indoor ultra-wideband communication systems.

*Wireless Network Design pp Cite as. Wireless communication has evolved significantly, over the past several decades, to meet the ever-growing demand for high data rates over the wireless medium.*

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Brunilda L.Metrics details.

Gianira A.PDF | Ultra-wide-band (UWB) signals are suitable for underlay communications, Coexistence Between UWB and Narrow-Band Wireless Communication Systems the scenarios, channel models, and modulation systems.