The decision feedback equalization is a technique widely used for removing ISI in frequency selective **multi**- path channels. The major problem in DFE is the so called error propagation; a decision error propagating through the feedback filter enhances ISI instead of cancelling it. Thus, a single error may cause a burst of errors in subsequent decisions. As reported in [5], the performance loss due to this phenomenon is approximately 2 dB for some channels. However, the existing **blind** algorithms, originally designed for transversal equalizers [6], [7], cannot be directly applied with a recursive **equalizer**, such as a DFE, because of the phenomenon of error propagation that characterizes a decision feedback updating. Namely, the enormous number of errors at the start of equalization restricts the use of **blind** adaptation to the case of a mild **channel**. Recently, several authors have presented various approaches to over- come this major defect of decision feedback **blind** equaliz- ers [7]-[10]. Therefore, the use of soft decisions to mitigate error propagation in a conventional DFE is considered for application to **blind** equalization in this paper.

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When T/2 Fractionally Spaced **blind** Equalization Algorithm based Constant Modulus Algorithm (T/2-FSE- CMA) is employed for equalizing higher order Quadrature Amplitude Modulation signals (QAM), it has dis- advantages of low convergence speed and large Mean Square Error (MSE). For overcoming these disadvan- tages, a Modified T/2 Fractionally Spaced **blind** Equalization algorithm based on Coordinate Transformation and CMA (T/2-FSE-MCTCMA) was proposed by analyzing the character of 16QAM signal constellations. In the proposed algorithm, real and imaginary parts of input signal of T/2 fractionally spaced **blind** **equalizer** are equalized, respectively, and output signals of **equalizer** are transformed to the same unit circle by coordi- nate transformation method, a new error function is defined after making coordinate transformation and used to adjust weight vector of T/2 fractionally spaced **blind** **equalizer**. The proposed algorithm can overcome large misjudgments of T/2 fractionally spaced **blind** equalization algorithm for equalizing **multi**-modulus higher order QAM. Simulation results with underwater acoustic **channel** models demonstrate that the pro- posed T/2-FSE-MCTCMA algorithm outperforms T/2 Fractionally Spaced **blind** Equalization algorithm bas- ed on Coordinate Transformation and CMA (T/2-FSE-CTCMA) and the T/2-FSE-CMA in convergence rate and MSE.

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The use of multiple transmit and receive antennas, commonly known as MIMO, has been shown to increase the capacity of a transmission link, [1]. This extra capacity can be exploited to increase the diversity gain of the system. The work in [2] proposed the STBC transmit diversity scheme, which is ca- pable of maximising the diversity over frequency-flat MIMO channels, whose respsonses between pairs of transmit and re- ceive antennas can be characterised by a complex gain factor. For high data rate service, most channels cannot be con- sidered frequency-flat anymore but are dispesrive, causing inter-symbol interference. In order to exploit diversity in such an environment, a number of variations on the classical STBC encoding have been proposed. OFDM can decompose a frequency-selective **channel** by introducing subcarriers and a cyclic prefix into a number of individual narrowband trans- mission channels, which can each be STBC encoded [3, 4]. The drawback of OFDM systems is in general the sensitiv- ity to synchronisation errors and their large peak-to-average power ratio. Single-carrier time domain approaches were first proposed by [4], whereby the STBC structure was ap- plied to a window of symbols, which is, after a guard in- terval, repeated as a complex conjugate and time reversed version [5]. However, time-reversal (TR) STBC is sensitive if the **channel** is doubly-dispersive, e.g. frequency selective and time-varying [6].

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The performance of DE based algorithm is better in comparision to BFO based algorithm which is evident from Fig.2 to Fig.5. Hence in the present work effect of various mutation startigies used in various types of DEs on **channel** equalization problem is also carried out. For this study four variants of DE based algorithms are used which are described mathematically in equations (4) to (7). For all four kinds of DEs the MSE floor for both CH1 and CH2 for NL1 and NL2 are ploted in Fig. 6 to Fig. 9 and then the BER vs. SNR plot are ploted in Fig. 10 to Fig. 13.

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This **equalizer**, henceforth referred to as the HNN MLSE **equalizer**, iteratively mitigates the eﬀect of ISI, producing near-optimal estimates of the transmitted symbols. The proposed **equalizer** is evaluated for uncoded BPSK and 16-QAM modulated single-carrier mobile systems with extremely long memory—for (CIRs) of multiple hundreds— where its performance is compared to that of an MMSE **equalizer** for BPSK modulation. Although there currently exist various variants of the MMSE **equalizer** in the literature [16–21]—some less computationally complex and others more e ﬃ cient in terms of performance—the conventional MMSE is nevertheless used in this paper as a benchmark since it is well-known and well-studied. It is shown that the performance of the HNN MLSE **equalizer** approaches unfaded, zero ISI, matched filter performance as the e ﬀ ective time-diversity due to multipath increases. The performance of the proposed **equalizer** is also evaluated for sparse channels and it is shown that its performance in sparse channels is superior to its performance in equivalent dense, or nonsparse, channels, (equivalent dense channels will be explained in Section 7) with a negligible computational complexity increase.

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degradation in performance caused by the ISI, a **blind** adaptive **equalizer**, may be implemented in those systems [1]-[12]. **Blind** de-convolution algorithms are essentially adaptive filtering algorithms designed such that they do not require the external supply of a desired response to generate the error signal in the output of the adaptive equalization filter [13]. The algorithm itself generates an estimate of the desired response by applying a non- linear transformation to sequences involved in the adaptation process [13]. **Blind** equalization methods are of great importance in digital signal communication systems, as they allow **channel** equalization at the receiver without the use of training signals which consume considerable **channel** capacity. In **blind** equalization, there is no wasted data on training symbols, therefore bandwidth is saved [6]. Since **blind** equalizers do not require any known training sequence for the startup period, they are also useful for point-to-multipoint network applications, such as the fiber to the curb (FTTC) systems [14]. Generally, **blind** methods are classified according to the location of their nonlinearity in the receiver [15]. We may classify **blind** equalization methods [15] as follows: 1) Polyspectral algorithms; 2) Bussgang-type algorithms; 3) Probabilistic algorithms. In the first type, the non- linearity is located at the output of the **channel**, right before the equalizer’s filter. The non-linearity has thus the function of estimating the **channel** and feeding that information to the **equalizer** for adaptation purposes. In the second type, the nonlinearity is found at the output of the equalizer’s filter and it is memoryless function. Among Bussgang type algorithms we may find Godard’s algorithm [7] which will also used in this paper. In the third type, the nonlinearity is combined with the data detection process. Algorithms with the third type can extract considerable information from relatively little data [15], but this is often accomplished at a huge com- putational cost.

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In addition, we also analyzed the "rotational" behavior of several blind algorithms using a torque concept, where we assess the susceptibility of the equalizer output constellation to co[r]

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Let us first plot in Figure 2 some of the optimum constellations obtained during our simulations when solving the optimization problem (14) over the considered **channel** model; moreover, we plot the suboptimum constellation utilized to implement our suboptimum strategy and the 8- PAM constellation obtained by applying to it the rhombic transformation. As in [2], we have found many local optima, some of them were rotated version of the constellations of Figure 2 while others appeared as their rhombic trans- formation. For K = 4 the locally optimum constellation set includes the conventional 4-QAM (β = 0) and 4- PAM (β = 1), as well as the 4-QAM subject to a rhombic transformation (β = − 0.4 + 0.3 j ); note that such constellations can be obtained by means of a rhombic transformation of the conventional 4-QAM (as also shown in Section 3.2), which has been utilized to implement our suboptimum strategy when K = 4. For K = 8, the optimum constellation set includes the noncircular 8-QAM found by Foschini et al. (β = 0.12 − 0.22 j ), one of the conventional 8-QAM scheme (β = 0) called “1-7” 8-QAM [2], the 8-PAM (β = 1) and the noncircular 8-QAM scheme that we call noncircular 8-QAM. In the following, in order to implement the rhombic-transformation-based constellation-optimization strategy, we resort to the rect- angular 8-QAM; we remember that, unlike 4-QAM, such a scheme cannot be transformed into the conventional uniform 8-PAM, but in the nonoptimum nonuniform 8- PAM (the optimality of uniform PAM over additive white Gaussian noise has been shown in [23]).

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The basic limitation of a linear **equalizer**, such as transversal filter, is the poor perform on the **channel** having spectral nulls. A decision feedback **equalizer** (DFE) is a nonlinear **equalizer** that uses previous detector decision to eliminate the ISI on pulses that are currently being demodulated. In other words, the distortion on a current pulse that was caused by previous pulses is subtracted. Figure 7 shows a simplified block diagram of a DFE where the forward filter and the feedback filter can each be a linear filter, such as transversal filter. The nonlinearity of the DFE stems from the nonlinear characteristic of the detector that provides an input to the feedback filter. The basic idea of a DFE is that if the values of the symbols previously detected are known, then ISI contributed by these symbols can be cancelled out exactly at the output of the forward filter by subtracting past symbol values with appropriate weighting. The forward and feedback tap weights can be adjusted simultaneously to fulfil a criterion such as minimizing the MSE. The DFE structure is particularly useful for equalization of channels with severe amplitude distortion, and is also less sensitive to sampling phase offset.

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High data rate transmission, spectral efficiency and reliability are necessary for future wireless communication systems. MIMO-OFDM (multiple input multiple output- orthogonal frequency division multiplexing) technology, has gained great popularity for its capability of high rate transmission and its robustness against **multi**-path fading and other **channel** impairments with the available power and bandwidth. A major challenge to MIMO-OFDM systems is how to obtain the **channel** state information accurately and promptly for coherent detection of information symbols and **channel** synchronization. When perfect knowledge of the wireless **channel** conditions is available at the receiver, the capacity has been shown to grow linearly with the number of antennas. In this work, MIMO-OFDM **channel** estimation is done by using a novel pilot signal that is well suited for wide band applications. Least Square (LS) and Minimum Mean Square error (MMSE) **channel** estimation methods are employed. **Blind** **channel** estimation and training sequence based estimation for fading channels (Rayleigh and Rician) using these two methods have been carried out. To improve the performance a new chaotic sequence is used for **channel** estimation. Finally the Mean square Error (MSE) analysis is done for SISO-OFDM and MIMO-OFDM and comparison is made between LS and MMSE methods through MATLAB simulation with chaotic pilot sequence and conventional pilot sequence. The proposed chaotic pilot sequence estimation gives superior performance.

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Abstract. In the pass years, IEEE 802.15.4 based Wireless Sensor Networks (WSNs) have received great attention and have been employed in many areas such as inventory checking, local monitoring and alarming etc. One of the key issues affecting WSN’s system performance is interference caused by devices operating with the same or different standards on the overlapping frequency within the 2.4 GHz ISM band. This paper addresses the coexistence problem, which is the key motivation for the necessity of flexible **channel** usage. A review of existing approaches being proposed to date supporting **multi**-**channel** utilization in IEEE 802.15.4 based WSNs is categorized and discussed. The paper also presents major functionalities needed in implementing **multi**-**channel** utilization.

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Aiming at the problem that ICA can only be confined to the condition that the number of observed signals is larger than the number of source signals; a single **channel** **blind** source separation method combining EEMD, PCA and RobustICA is proposed. Through the eemd decomposition of the sin- gle-**channel** mechanical vibration observation signal the multidimensional IMF components are obtained, and the principal component analysis (PCA) is performed on the matrix of these IMF components. The number of principal components is determined and a new matrix is generated to satisfy the over- determined **blind** source separation conditions, the new matrix input Robus- tICA, to achieve the separation of the source signal. Finally, the isolated sig- nals are respectively analyzed by the envelope spectrum, the fault frequency is extracted, and the fault type is judged according to the prior knowledge. The experiment was carried out by using the simulation signal and the mechanical signal. The results show that the algorithm is effective and can accurately di- agnose the location of mechanical fault.

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factor does in genetic algorithms. That is, a small number of L taps can be considered as active, given that the total number of rank 1 and rank 0 taps is less than an arbitrary Max NTap < L . The random picking of taps is necessary when operating under a dynamic multipath scenario, that is, when the receiver is under mobile operation. A quantitative measure of the multipath dynamics is the Doppler deviation. Under mobile operation, the **channel** impulse response varies periodically with a period given by approximately the inverse of the Doppler frequency. Thus, the **channel** frequency domain transfer function varies accordingly. Since the **equalizer** should ideally implement the **channel** inverse transfer function in order to cancel the multipath eﬀects, it follows that the **equalizer** taps must track the **channel** variations at nearly the Doppler rate. The DTA procedure reinforces the largest magnitude taps during the gradient convergence phase, and this action interlocks the active tap set even after the **equalizer** convergence. Therefore, when the **channel** is time variant, as is the case under mobile operation, it is necessary to refresh the active tap set population via random picking in order to cope with the dynamic **channel**. Algorithm 2 shows the proposed DTA.

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In conclusion, **multi**-**channel** **multi**-scale edge detection has been proposed to segment the retinal blood vessel. The performance of the proposed method is verified and validated by using two online databases, DRIVE and HRF. The results show that the proposed algorithm performs well for both databases. Therefore, the proposed algorithm can be a good base for blood vessel segmentation in many applications. As for the future work, the research will focus on improving the accuracy of the segmented output built upon the proposed method.

ABSTRACT: Single-carrier (SC) block transmission with cyclic prefix (CP) is a method with several advantages that has been incorporated into standards. This paper has analyzed the performance of **multi**-antenna SC-FDE under Alamouti signaling and cyclic-delay diversity (CDD). Our analysis shows that the characteristic of diversity it is depends on data block length and data transmission rate as well as on the **channel** memory and antenna configuration. At higher rates their diversity diminishes and full diversity is available to both CDD and Alamouti signalling below a certain rate threshold. From our investigation we say that at high rates Alamouti signalling provides twice the diversity of SISO SC-FDE, while the diversity of the SISO SC-FDE under the CDD diversity degenerates.

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T he Rake Receivers use maximum ratio combining and **multi**-user interference suppression to obtain a considerable increase in performance in DS–CDMA system such as WCDMA. WCDMA is the multiple access technique selected for the 3G mobile communications systems and it has a significant role in the research beyond 3G systems. WCDMA systems over wireless channels have to cope with fading multipath propagation, which makes the **channel** estimation an important issue in Rake receivers. Despite a significant amount of scientific literature on Rake receivers, there are still open problems regarding the multipath delay and coefficient estimation in hostile environments and the design of low -complexity DSP -based **channel** estimators for WCDMA applications [1]. Multipath fading is one of the major practical concerns in wireless communications. Multipath problem always exists in the mobile environment, especially for a mobile unit which is often embedded in its surroundings. The RAKE receiver has been used to reduce the multipath fading in a wideband spread spectrum mobile system [2]. However, the tap weights of the multipath **channel** model need to be estimated. We explore the possibility of using advanced signal processing algorithms to estimate the multipath channels and propose a least- squares approach to tap weight estimation based on chip rate **channel** estimates to investigate the performance of the RAKE receiver in a realistic mobile environment [2]. Weaker interfering users are modelled by adding white Gaussian noise to the **channel** [3]. The potent ional benefits of using an **equalizer** are an increasing capacity on the one hand as well as

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The computational complexity per detected symbol of the VA part in the proposed method (part (c)), along with the corresponding complexities of the LTE, the Volterra, and the RBF-DF equalizers considered here are shown in Table 5. Finally, Table 6 shows the total number of real opera- tions required for the processing of a received block con- sisting of 20 training samples (per energy zone) and 500 data symbols, for a two-tap **channel** (L = 2) with the 16-QAM signaling scheme. For the purposes of this compar- ison, 3 times 20 = 60 training samples are also assumed for the LTE and the Volterra equalizers, although, as we have al- ready seen, this is not realistic and in practice a much longer data set is needed for these methods. For these equalizers,

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iterative or turbo receivers. Each processing block in the traditional receiver outputs binary integer values resulting in the reliability information about the output symbols being lost. The performance of the receiver can be greatly improved if each block of the receiver outputs a posteriori probabilities (APP) or log likelihood ratios (LLR) of the symbols, that is, soft outputs. Much work in the design of soft output algorithms was encouraged by the need to provide soft inputs to the next processing stage. For example, a **channel** **equalizer** should generate soft outputs so as to increase the e ﬃ ciency of the soft input **channel** decoder. The **channel** decoder then not only provides APPs of the information bits but also provides APPs of the encoded bits. These APPs, known as extrinsic information, can be used after interleaving by the **equalizer** as prior probabilities, also known as intrinsic information, for the next iteration. This is the fundamental idea behind the turbo or iterative receiver, that is, the exchange of soft information. The performance of the receiver improves as the number of iterations increases between the blocks of the receiver. Interested readers can refer to [3–8] for detailed information on this subject. The first turbo **equalizer** of its kind was presented by Douillard et al. [9] to combat multipath using the soft output Viterbi Algorithm (SOVA), where soft information is exchanged between the **equalizer** and decoder. A complete maximum a posteriori- (MAP-) based turbo **equalizer** was proposed by Bauch et al. [10] where it was shown that for a 5-tap **channel** exhibiting a deep spectral null, the performance of the receiver after 8 iterations between the MAP **equalizer** and MAP **channel** decoder is very close to that of a code on a non-ISI **channel**; however, this cannot be possible when the **channel** is unknown to the receiver and possibly time varying. A low complexity iterative **equalizer** structure using minimum mean square error (MMSE) criterion

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In Block Data Transmission System the noise performance of the Block Linear **Equalizer** depends on the **channel** distortion and the size of the transmitted signal block. The algorithm presented in this paper adaptively controls the block size by estimating the **channel** distortion from the estimated impulse response of the **channel**. For higher values of **channel** distortion the block size is reduced and for smaller values of **channel** distortion the block size is increased. This is done at the transmitter through a look up table. The resulting block linear **equalizer** is called the Adaptive Block Linear **Equalizer** (ABLE). It achieves considerable advantage in tolerance to noise over the BLE and has a higher information transmission rate for the same element transmission rate. This advantage is achieved with only a slight increase in equipment complexity.

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