AEU - International Journal of Electronics and Communications (16180399)149
The quality of direction of arrival (DOA) estimation is affected by factors such as noise and interferences in wireless communication and other systems. By employing the benefits of cyclostationary signal analysis, which includes immunity to noise and interferences, in this paper, a simple high-resolution DOA estimation method is proposed. Most of the cyclostationary-based DOA estimation methods depend on the statistical properties of the received signals (e.g., the cyclic correlation matrix). Unlike them, in the proposed method, the received signal directly enters a DOA estimation loop containing a normalized least mean square (NLMS) adaptive algorithm. The method has high resolution and performance and low computational complexity in comparison with its counterparts. Moreover, it is befitting for signals in the presence of noise (Gaussian and impulsive) and interferences. Simulation results validate the efficiency of the proposed method from different aspects in comparison with other related studies. © 2022 Elsevier GmbH
Signal, Image and Video Processing (18631711)15(4)pp. 835-842
The purpose of this paper is to develop a novel method based on recursive least squares (RLS) adaptive algorithm for progressive image transmission (PIT). The image is divided into non-overlapping blocks. Having an agreed vector sequence between the transmitter and the receiver, each block is related to a regressive model. Meanwhile, at the transmitter the blocks are estimated using the RLS algorithm. The high correlation between error vectors, regarding to the RLS execution, causes a very high compression rate in their transmission. The error vectors at the receiver are used to run the RLS algorithm and to estimate the image in a same manner. The method is easy to implement with a low computational complexity and achieves high quality, compared to other well-known methods. In comparison with its counterparts, simulation results show how efficient the proposed method is. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
IEEE Transactions on Communications (00906778)69(5)pp. 2851-2862
Bit rate fairness is an important concern in current G.fast and the forthcoming fifth generation DSL systems where the frequency is extended to hundreds of MHz and crosstalk couplings reach unprecedented levels. In this paper, we study the price of fairness (PoF) in dynamic spectrum management (DSM) enabled DSL systems. We propose optimal low complexity PoF-constrained max-min fairness (MMF), weighted max-min fairness (WMMF), proportional fairness (PF), and (p,α) -PF algorithms. The proposed algorithms inherently provide weight factors which can be used to reduce the computational complexity of user encoding or decoding order optimizing algorithms used in nonlinear vectoring. Our simulation results show that although PoF grows exponentially with the minimum bit rate, fairness can be improved considerably with a relatively small price in DSM level 2 spectrum balancing and particularly in DSM level 3 using minimum mean square error (MMSE) generalized decision feedback equalizer (GDFE). It is also seen that the proposed optimal PoF-constrained MMF algorithm can reach the solutions of PF and (p,α) -PF measures for some PoF. That is, the PoF-constrained MMF or WMMF algorithms can be used instead of the non-linear PF and (p,α) -PF measures, which often result in computationally intensive solutions. © 1972-2012 IEEE.
Infrared Physics and Technology (13504495)99pp. 222-230
In this paper, a novel adaptive algorithm for target detection in hyperspectral images (HSIs) is proposed. In a general classification, the proposed method belongs to the category of those methods which are not based on the statistical moments of the observed HSI (e.g. correlation or covariance matrix). The main processing burden of the proposed method is over a known set of spectral signatures. Assuming a linear spectral mixing model, the proposed method takes a set of spectral signatures which one of them relates to the target material and the others relate to the background materials. Based on an adaptive approach, the normalized least mean square (NLMS) adaptive algorithm is engaged to estimate a weight vector which is almost orthogonal to the background materials spectral signature whereas it makes an absolutely non-orthogonal pair with the target material spectral signature. The estimated weight vector is multiplied by the observed HSI to make the final decision. One synthetic and two real hyperspectral images are considered to evaluate the performance of the proposed method. The evaluation results show that the proposed method outperforms its counterparts. © 2019
Signal Processing (01651684)155pp. 108-129
Kalman filter (KF) as a linear estimator which is used in super-resolution (SR) problems, suffers from high computational costs and storage requirements. To gain appreciable success in the elimination of these two challenges, this paper advances a SR framework employing KF in the frequency domain, while no resort is made to any approximations or extra assumptions in the dynamic system modeling and statistical matrices. Generally, previous KF-based SR methods organized the system with huge-sized matrices in the spatial domain, following which they tried to reduce the system dimension using approximation and/or limitation on point spread function (PSF). In this study, first, several small-dimension dynamic systems are separately made in the frequency domain supporting space-invariant PSFs of an arbitrary form and size. Then, the acquired small-dimension KF estimators are applied rather than the traditional huge-dimension one. These will greatly reduce computational complexity, decrease storage requirements allowing parallel implementation as well. Furthermore, our proposed SR framework can be used to produce high resolution image of an expedient size, that is, a scalable SR. Experimental results with both simulated and real world sequences indicate that our proposed framework works more effectively than the other compared methods, especially in fine details restoration. © 2018 Elsevier B.V.
AEU - International Journal of Electronics and Communications (16180399)84pp. 13-20
Cyclic frequency detection of cyclostationary signals and its application in modulation parameters estimation is the aim of this paper. A simple frequency shifted (FRESH) model is structured. The normalized least mean square (NLMS) algorithm is engaged to estimate the unknown weights of the considered model. The norm of the parameter estimate is related to the model accuracy. A high (low) value indicates that the FRESH structure is based on an accurate (inaccurate) cyclic frequency and the weight estimate of the NLMS algorithm converges to a nonzero (zero) vector. Accordingly for two given values which only one of them is the true cyclic frequency of the received signal, two FRESH structures are considered and simultaneously two NLMS algorithms are executed. If the norm of the weight estimate of the first algorithm exceeds the resulted norm of the second one, according to the proposed method, the first given value is the true cyclic frequency and vice versa. The method is used to estimate the modulation parameters of three different problems: chip rate estimation of a spread spectrum signal, carrier frequency and bit rate estimation of BPSK and QPSK signals, and symbol rate estimation of OFDM signals. © 2017 Elsevier GmbH
Digital Signal Processing: A Review Journal (10954333)72pp. 19-43
As there are data redundancies in successive frames in a multi-frame super resolution (SR) algorithm, one can expect that discarding some of these superfluous frames would have no impact on the quality of the high resolution (HR) output image. The present paper presents an efficient algorithm for selecting the proper combination of the minimum frames required for multi-frame SR algorithms so as to not only preserve the quality of the obtained HR output, but also reduce the SR procedure complexity and memory. To achieve this, the present study first seeks to prove that minimizing the spectral interference between the selected frames for SR procedure will result in maximizing the HR output power. Then, the criterion for measuring the Upper Bound on Spectral Interferences (UBSI) among the selected frames for SR procedure is presented; the formulation is expressed in such a way that it can be extended to global sub-pixel translations between frames. Our proposed frame selection algorithm evaluates all candidate combinations from input frames so that the best option capable of minimizing the UBSI can be selected. In order to evaluate our proposed frame selection algorithm, five well-known SR image reconstruction methods are applied both in four standard simulated images and in three well known real video sequences, employing two different procedures: Using our proposed frame selection algorithm and otherwise. The obtained results indicate that when our proposed frame selection algorithm is applied, the quality of the HR output images is preserved tantamount to considering all available frames. Besides, the computational complexity of the SR algorithms is dramatically reduced adopting the proposed frame selection algorithm, for the number of frames engaged in the SR is diminished. Also compared with the SR algorithms presented in the literature, our proposed frame selection method takes relatively negligible time to execute. © 2017 Elsevier Inc.
IEEE Communications Letters (10897798)22(4)pp. 760-763
In this letter, a very simple and low-complex method is proposed for direction-of-arrival (DOA) estimation. While most of the existing DOA estimators are based on the statistical properties of the received signals (e.g., covariance matrix), the proposed method directly uses the observations. The normalized least mean squares adaptive algorithm makes the fundamental of the method. All directions are estimated one after another. The method outperforms its current counterparts from computational complexity point of view. Some simulation scenarios are presented to show the performance of the proposed method. © 1997-2012 IEEE.
Wireless Personal Communications (1572834X)95(3)pp. 3167-3184
This paper focuses on beamforming problem in a cooperative cognitive radio communication system where primary users (PUs) coexist with a secondary user (SU). An adaptive partial feedback structure for computing the channel state information is proposed. PUs and SU receivers execute their associated normalized least mean square adaptive algorithms. The output of each algorithm contains an estimate of the related channel coefficients. The receivers also reply the quantized step-size values of their algorithms via feedback channel to the transmitter, which empower it to execute these algorithms simultaneously. The transmitter iteratively employs the feedback information to estimate the channel weights and uses them for its beamforming scheme, in such a way that the SU receiver’s channel gain is maximized while the interference on PUs is maintained below a predefined level. Analysis of the proposed method shows that, with a simple modification, the required bits in feedback channel are considerably reduced. The method is applicable for time-varying systems. Besides, when the cross correlation matrix is the only available channel information between primary and secondary networks, a simple and compatible beamforming scheme is proposed. Simulation results evaluate the reasonable performance of the proposed method. © 2017, Springer Science+Business Media New York.
Wireless Personal Communications (1572834X)97(2)pp. 2781-2797
In general, signals transmitted by primary users (PUs) in a cognitive radio network have cyclostationary characteristics, whereas, the noise signals do not have any cyclostationary characteristics. Thus, detecting the existence of the PUs can be performed by measuring the cyclostationarity of the signals which are present in the communication channels. In this paper, we propose a sensing algorithm for secondary users (SUs) that uses a set of normalized least mean square (NMLS) adaptive filters in order to estimate the signal in the communication channel from its frequency shifted samples. When the received signal is cyclostationary, i.e. the PUs are transmitting, the norm of the NMLS filters’ weights at the related SU is anticipated to be nonzero. On the other hand, when the signal is totally the noise, the norm converges to zero. Therefore, in the proposed algorithm, the sensing is made by comparing the norm of weights to a threshold. We derive the probability of detection and false alarm and by simulations, we compare the performance of our algorithm to other known sensing algorithms with respect to the detection probability and complexity. © 2017, Springer Science+Business Media, LLC.
IEEE Communications Letters (10897798)20(10)pp. 2015-2018
A cyclostationary feature-based wideband spectrum sensing is proposed in which cyclic frequency offset is considered. The received signal passes through a rough and flexible filter which its effective band tuned to a specific part of the received signal spectrum. It belongs to a target signal, which possibly exists in the received signal. Some cyclic frequencies of the target are employed to derive the normalized least mean square algorithm that estimates the filter output from its frequency shifted samples. Based on the weight estimate's norm, the algorithm is tuned to find the accurate cyclic frequencies. If it succeeded, it means that the target signal is present. On the other hand, a complete search without major weights estimate means that the target signal is absent. In addition, the overall system, named cyclic frequency lock loop can be reformed as a demodulator for sinusoidal frequency modulation signals. The method is very easy to implement and has less computational complexity comparing with its other spectrum sensing counterparts. Simulation results validate the efficiency of the proposed method. © 2016 IEEE.
Sabahi, M.F.,
Shahtalebi, K.,
Rezaei M.J.,
Zaeem R.M.,
Sadeghi R.,
Rezaei M.J.,
Rezaei M.J.,
Sabahi, M.F.,
Sabahi, M.F.,
Shahtalebi, K.,
Shahtalebi, K.,
Zaeem R.M.,
Zaeem R.M.,
Sadeghi R.,
Sadeghi R. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 124-127
In this paper, a new fairness index to measure resource allocation performance for real-time/delay-tolerant applications is introduced. This index can suggest a new approach for resource allocation. There are several methods have been previously introduced in the literature for resource allocation in cellular networks. Fairness index have an important role to evaluate the performance of these methods. Here, we focus on utility function based resources allocation and related algorithms. According to the introduced method, the base station (BS) allocates resources based on different services requirements. Because of using the appropriate utility function for each application, the requested quality-of-services (QoS) are satisfied. The new well-defined fairness index shows that the proposed method has a good performance for different real-time/delay-tolerant applications. Additionally, numerical results show that this approach is able to improve other important indicators such as throughput and mean opinion score (MOS) as well. © 2015 IEEE.
Characterizing the satellite beam footprint boundary can be used to measure the overlap of the beam coverage area of a satellite with that of other communication systems. A crucial application of this is in emerging cognitive satellite communication systems. In this paper, an analytical model based on ray tracing is proposed for specifying the boundary and the size of the satellite beam footprint and the distance of the footprint center from the beam center on Earth's surface. These characteristics are primarily a function of four parameters, namely, the coordinates of satellite, the beam cross-sectional shape, the coordinates of beam center, and the beamwidth. These parameters are reduced to only two parameters of Earth central angle between the nadir and the beam center and the beamwidth. Numerical results show that by increasing any of the two parameters the asymmetry of the footprint, its size, and the distance between the footprint center and the beam center increase. © 2016 IEEE.
Transactions on Emerging Telecommunications Technologies (21613915)27(10)pp. 1380-1395
In this paper, we propose an efficient spectrum leasing scheme for cognitive radio networks in which the leasing process is carried out by employing two generally non-identical secondary users. The first secondary user is selected for cooperating with the primary network, and the second one is offered the released spectrum to perform the secondary transmission. The two mentioned secondary users are selected independently. Both the decode-and-forward and the amplify-and-forward relaying protocols are considered in this paper. Moreover, a protocol selective relaying scheme named decode- or amplify-and-forward (DAF), which smartly switches between the two protocols, is applied. We show that the disjoint secondary user selection and the protocol selective relaying result in an enhanced outage performance for both the primary and secondary networks as well as an increased achievable transmission rate for the secondary network. Numerical and simulation results are presented to evaluate the performance improvement of the proposed scheme. Using the simulation results, we also investigate the effect of disjoint secondary user selection on fairness considerations of the secondary network. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Wireless Personal Communications (1572834X)85(3)pp. 1351-1365
To successfully design and evaluate the performance of multiple-input multiple-output (MIMO) mobile-to-mobile (M-to-M) communication systems, it is necessary to have detailed knowledge of the multipath fading channel and its statistical properties. In this paper, we propose a full-three-dimensional (3-D) reference model for wideband MIMO M-to-M communication channels. From the reference model, the corresponding space-time-frequency correlation function is derived, assuming 3-D non-isotropic and single-bounce scattering environment. This model includes many existing models as special cases. Some numerical results are presented to validate special cases of the proposed reference model. © 2015, Springer Science+Business Media New York.
Wireless Personal Communications (1572834X)85(4)pp. 1869-1882
This paper investigates reducing the computation complexity in resource allocation of cognitive radio networks. The orthogonal frequency division multiple access scheme is assumed. Secondary users are permitted to capture any portion of the bands while their interference to the primary users remains below a given threshold level. A very simple method with low complexity for resource allocation is proposed in which the throughput logarithmic function is changed into a linear form. The method is applicable in a wide range of problems where the efforts are maximizing the throughput under power budget and interference constraint. Although the proposed method achieves the optimal solution in low channel-gain-to-noise ratio (CNR) cases, the results for high CNRs are also satisfactory. Comparisons between the proposed method and previous works via simulation show the superior performance of the proposed method. © 2015, Springer Science+Business Media New York.
IET Communications (17518628)9(12)pp. 1442-1449
In this study, the authors examine resource allocation in an orthogonal frequency-division multiple-access-based cognitive radio (CR) network which dynamically senses primary users (PUs) spectrum and opportunistically uses available channels. The aim is resource allocation such that the CR network throughput is maximised under the PUs maximum interference constraint and cognitive users (CUs) transmission power budget. This problem is formulated as a mixedinteger non-linear programming problem which is NP-hard in general and infeasible to solve in real-time. To reduce the computational complexity, the authors decouple the problem into two separate steps. After initial power allocation, in the first step, an adaptive algorithm is employed to assign subcarriers to the CUs toward throughput maximisation by using these initial powers. In the second step, power is allocated optimally to the assigned subcarriers. Simulation results show that the proposed method nearly achieves the optimal solution in a small number of iterations meaning significant reduction in the computational complexity. © The Institution of Engineering and Technology 2015.
Shahtalebi, K.,
Haqiqatnejad a.r., A.R.,
Haqiqatnejad a.r., A.R.,
Haqiqatnejad a.r., A.R.,
Shahtalebi, K.,
Shahtalebi, K. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 202510pp. 438-443
In this paper, we are interested in a cross-layered spectrum scheduling for the secondary network under the spectrum leasing process. The process selects a secondary user to access the released spectrum, independent of the user who has participated in the cooperative primary transmission. At first, a cross-layer analysis to derive a lower bound on the overflow probability of the secondary network is given. It will be the achievable optimal probability of overflow for the secondary network. The result leads us to introduce the cross-layer secondary user selection method which is based on ?-algorithms. While physical layer based approaches conventionally take the channel state into account, the cross-layer secondary user selection method considers both the achievable rate and the queue-length of each secondary user. We show that the cross-layer approach can be considered as a sub-optimal solution to achieve the optimal probability of overflow. Simulation results are presented to validate our analysis and evaluate the efficiency of the proposed approach. © 2015 IEEE.
Sabahi, M.F.,
Shahtalebi, K.,
Zabihi P.,
Zabihi P.,
Zabihi P.,
Sabahi, M.F.,
Sabahi, M.F.,
Shahtalebi, K.,
Shahtalebi, K. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 1166-1171
In this paper we have integrated Game theory framework, effective capacity theory and interference minimization for power allocation analysis over fading multiple access channels (MAC). Game theory is a powerful tool for modeling selfish behavior of competitors in resource allocation contest and can be used effectively in designing and modeling behavior of users in distributed resource allocation situations. Effective capacity is a useful tool that can impressively characterize delay constraint for wireless links. Interference consideration is too critical in multiple access channels and dynamic spectrum access policies. Here we set up a non-cooperative game and then we derive Nash equilibrium by analyzing the two player game. In the sense of simultaneous transmission our proposed policy caused less interference to users and better rates in Shannon capacity region achieved while considering QoS of users. We investigate the performance of our scheme for cases in which the QoS constraints of users increased and then we compare our scheme with the QoS-Driven power allocation game. © 2014 IEEE.
Previous works in spectrum leasing for centralized cognitive radio networks (CRNs) are based on selecting a secondary user as a relay to cooperatively transmit the primary data and then leasing the released spectrum due to the cooperation to the selected user. In this paper, we propose a new spectrum leasing scheme to improve the throughput and thus the spectrum efficiency of the secondary system. The proposed approach selects two secondary users for cooperation and secondary transmission independently, which are the best users towards the primary and secondary systems, respectively. We show that the outage performance of the secondary system is enhanced as a result of the independent secondary user selection. Analytical and simulation results are presented to verify the efficiency of our approach and performance improvement in the secondary system. © 2014 IEEE.
International Journal Of Antennas And Propagation (16875877)2012
Analysis and design of multielement antenna systems in mobile fading channels require a model for the space-time cross-correlation among the links of the underlying multipleinput multiple-output (MIMO) Mobile-to-Mobile (M-to-M) communication channels. In this paper, we propose the modified geometrical two-ring model, a MIMO channel reference model for M-to-M communication systems. This model is based on the extension of single-bounce two-ring scattering model for flat fading channel under the assumption that the transmitter and the receiver are moving. Assuming single-bounce scattering model in both isotropic and nonisotropic environment, a closed-form expression for the space-time cross-correlation function (CCF) between any two subchannels is derived. The proposed model provides an important framework in M-to-M system design, where includes many existing correlation models as special cases. Also, two realizable statistical simulation models are proposed for simulating both isotropic and nonisotropic reference model. The realizable simulation models are based on Sum-of-Sinusoids (SoS) simulation model. Finally, the correctness of the proposed simulation models is shown via different simulation scenarios. Copyright © 2012 Gholamreza Bakhshi et al.
IET Communications (17518628)6(16)pp. 2740-2749
Partial feedback in multiple-input multiple-output communication systems provides tremendous capacity gain and enables the transmitter to exploit channel condition and to eliminate channel interference. In the case of severely limited feedback, constructing a quantised partial feedback is an important issue. To reduce the computational complexity of the feedback system, this study introduces an adaptive partial method in which at the transmitter, a set of easy to implement least-square adaptive algorithms is engaged to compute the channel state information. In the proposed method, at the receiver, the time-varying step-sizes of the algorithms are computed and replied to the transmitter via a reliable feedback channel. The transmitter iteratively employs this feedback information to estimate the channel weights. This method is independent of the employed space-time coding schemes and gives all channel components. Complementary solution is given to reduce the computational complexity and simulation examples are given to evaluate the performance of the proposed method. © The Institution of Engineering and Technology 2012.
IET Communications (17518628)4(16)pp. 1963-1973
In this study, we propose a least mean square-partial parallel interference cancellation (LMS-PPIC) method named parallel LMS-PPIC (PLMS-PPIC) in which the normalised least mean square (NLMS) adaptive algorithm with optimised chip time-varying step-size is engaged to obtain the cancellation weights. The former LMS-PPIC method is based on fixed not optimised step-size, which causes propagation of error from one stage to the next one and increases the bit error rate (BER). The unit magnitude of the cancellation weights is the principal property in our step-size optimisation. To avoid computational complexity a small set of NLMS algorithms with different step-sizes are executed. In each iteration the parameter estimate of that NLMS algorithm which the elements magnitudes of its cancellation weight estimate have the best match with unit is chosen. Magnificent decrease in BER is achieved by executing the proposed method. Moreover PLMS-PPIC like former LMS-PPIC method comes to practice only when the channel phases are known. When they are unknown, having only their quarters in (0, 2π), we propose modified versions of LMS-PPIC and PLMS-PPIC to find the channel phases and the cancellation weights simultaneously. Simulation scenarios are given to compare the performance of our methods with that of LMS-PPIC in two cases: balanced channel and unbalanced channel. The results show that in both cases the proposed method outperforms LMS-PPIC, especially for high processing gains. © 2010 © The Institution of Engineering and Technology.
Shahtalebi, K.,
Bakhshi, G.,
Rad, H.S.,
Bakhshi, G.,
Bakhshi, G.,
Shahtalebi, K.,
Shahtalebi, K.,
Rad, H.S.,
Rad, H.S. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025
Analysis and design of multi-element antenna systems in mobile fading channels require a model for the space-time cross-correlation among the links of the underlying multipleinput multiple-output (MIMO) Mobile-to-Mobile (M-to-M) communication channel. In this paper, based on the Modified Geometrical Two-Ring (MGTR), a Full-Three-Dimensional (3-D) MIMO channel reference model for M-to-M communication systems is proposed. In the proposed method named the geometrical single-bounce two-sphere (SBTS) model, both transmitter and receiver are moving components. Assuming 3-D Non-isotropic and single-bounce scattering model, a closed-form expression for the space-time cross-correlation function (CCF) between each two sub channels is derived where includes many existing correlation models as special cases. Some simulation results are presented as special cases of the derived CCF. © 2009 IEEE.
Shahtalebi, K.,
Bakhshi, G.,
Saadat, R.,
Bakhshi, G.,
Bakhshi, G.,
Saadat, R.,
Saadat, R.,
Shahtalebi, K.,
Shahtalebi, K. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 409-413
Analysis and design of multi-element antenna systems in mobile fading channels require a model for the space-time cross-correlation among the links of the underlying multipleinput multiple-output (MIMO) Mobile-to-Mobile (M-to-M) communication channel. In this paper, we propose a MIMO channel reference model for M-to-M communication systems that we call it as the modified geometrical two-ring model. This model is based on the extension of single-bounce two-ring scattering model for flat fading channel under the assumption that the transmitter and the receiver are moving. Assuming an isotropic and singlebounce scattering model, a closed-form expression for the spacetime cross-correlation function (CCF) between any two sub channels is derived. The proposed model provides an important framework in M-to-M system design, where includes many existing correlation models as special cases. Some numerical results are presented as special cases of the derived CCF. ©2008 IEEE.
IEEE Wireless Communications and Networking Conference, WCNC (15253511)pp. 392-396
Parallel least mean square-partial parallel interference cancellation (PLMS-PPIC) is a partial interference cancellation which employs adaptive multistage structure [1]. In this algorithm the channel phases for all users are assumed to be known. Having only their quarters in (0, 2π), a modified version of PLMS-PPIC is proposed in this paper to simultaneously estimate the channel phases and the cancellation weights. Simulation examples are given in the cases of balanced, unbalanced and time varying channels to show the performance of the modified PLMS-PPIC method. © 2008 IEEE.
Shahtalebi, K.,
Bakhshi, G.,
Rad, H.S.,
Shahtalebi, K.,
Bakhshi, G.,
Rad, H.S. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 209-213
Least mean square-partial parallel interference cancellation (LMS-PPIC) is a partial interference cancellation using adaptive multistage structure in which the normalized least mean square (NLMS) adaptive algorithm is engaged to obtain the cancellation weights. The performance of the NLMS algorithm is mostly dependent to its step-size. A fixed and non-optimized step-size causes the propagation of error from one stage to the next one. When all user channels are balanced, the unit magnitude is the principal property of the cancellation weight elements. Based on this fact and using a set of NLMS algorithms with different step-sizes, the parallel LMS-PPIC (PLMS-PPIC) method is proposed. In each iteration of the algorithm, the parameter estimate of the NLMS algorithm is chosen to match the elements' magnitudes of the cancellation weight estimate with unity. Simulation results are given to compare the performance of our method with the LMS-PPIC algorithm in three cases: balanced channel, unbalanced channel and time varying channel. ©2008 IEEE.
SIGNAL PROCESSING (01651684)87(6)pp. 1288-1300
The idea of minimizing the variance in biased estimation along with controlling the gradient of bias is well established for the case of singular Fisher information matrix (FIM) in order to find the biased estimators. In this paper, the biased Cramer-Rao lower bound (BCRLB) is used to derive and study the estimate of unknown parameters in a linear model with a known twice differentiable additive noise probability density function (PDF). Even if the additive noise is not Gaussian, we show that the derived linear estimators (not unique) are linear functions of the observations (where a constant number is inserted into observation vector) in a particular form. Examples are included to illustrate the estimators performances. We show that a biased estimator obtained by optimization of BCRLB is not necessary satisfactory in a general case; therefore, additional considerations must be taken into account when using this approach. For the case where the PDF of the additive noise is not differentiable, such as uniformly distributed or time invariant magnitude noises, an asymptotical approach is given to find the estimators. As an example, we evaluate the performance of the derived adaptive filter for a first-order Markov time varying system. If the FIM is singular, we use the method of singular value decomposition (SVD) to extract the parameter estimate of the linear models. For example we show that in a linear model, parameter estimation based on single observation leads to the normalized least mean square (NLMS) algorithm. In this example using BCRLB optimization, we find the relation between the step-size of the NLMS algorithm and the bound of the bias gradient matrix. (c) 2006 Elsevier B.V. All rights reserved.
In this paper, the biased Cramér-Rao Lower Bound (BCRLB) is used to derive the estimate of unknown parameters in a linear model with an arbitrary known additive noise probability density function (PDF). We show that the derived linear estimators (not unique) are linear functions of the observations. Examples are included to illustrate their performances. We show that a biased estimator obtained by optimization of BCRLB is not necessary satisfactory in a general case; therefore, additional considerations must be taken into account. If the Fisher information matrix (FIM) is singular, we use the method of singular value decomposition (SVD) to extract the parameter estimate of linear model. For example we show that in a linear model, parameter estimation based on single observation leads to the normalized least mean square (NLMS) algorithm. In this example using BCRLB optimization, we find the relation between the step size of the NLMS algorithm and bound of bias gradient matrix. ©2005 IEEE.
The performance of an adaptive filter is restricted by the statistical behavior of the additive noise. The aim of this paper is to improve the convergence speed and steady state error of the Set-Membership Normalized Least Mean Square (SM-NLMS) algorithm in a colored noise environment. The noise is assumed to follow an Auto-Regressive (AR) model with bounded excitation uncorrelated samples. Without information about the noise parameters, the traditional SM-NLMS algorithm results in an unsatisfactory performance. A new simple SM algorithm is introduced to estimate the channel and the noise parameters simultaneously. The proposed algorithm efficiently exploits the redundant information of the noise to combat the noise. Theoretical results and simulations illustrate that the proposed algorithm has remarkable performance improvement over the NLMS and the traditional SM-NLMS algorithms. This proposed is successfully applied to a decision-directed algorithm for QPSK communication scheme over a ISI channel with heavily colored noise environment. © 2004 IEEE.
Canadian Conference on Electrical and Computer Engineering (08407789)3pp. 1229-1232
A three-dimensional (3D) model is proposed for Multiple-Input Multiple-Output (MIMO) microcell Rayleigh fading channels with an ample number of scatterers. We assume appropriate probability density functions (pdfs) for relevant physical parameters of the complex scattering media. The impact of these parameters are discussed using the expression of the Correlation Function (CF) between each of the two sub-channels of the MIMO channel. The CF is decomposable into several components that describe spatial, temporal, and frequency characteristics of the MIMO communication system. Such a decomposition allows easier investigation and gives a better understanding of the full potential of MIMO wireless communications. The describing components do not always have closed-form expressions. Therefore, closed-form expressions are obtained for some special cases. In practice, a linear convex combination of the expressions from these cases can approximate almost any model of microcellular environments. The proposed model is a generalization of several existing models including the Jake's/Clark model.
IEEE Signal Processing Letters (10709908)9(11)pp. 348-351
A set-membership (SM) normalized least-mean-square (NLMS) (SMNLMS) algorithm is developed using SM theory in the class of optimal bounding ellipsoid (OBE) algorithms. This signed version of NLMS algorithm requires a priori knowledge of a bound for the error magnitude, which is unknown in most applications. A very simple algorithm is proposed for the case in which the unknown magnitude of the measurement noise is slowly time-varying. The proposed algorithm is able to extract the noise magnitude information and exploit this magnitude to enhance or accelerate the learning proeess without risk of overbounding or performance loss due to underbounding. The performance of the proposed algorithm is compared with that SMNLMS using some simulation examples.
Scientia Iranica (23453605)9(4)pp. 378-384
In this paper, set-membership identification is used to derive a simple algorithm which is a sign version of the normalized least mean square algorithm. Convergence analysis is carried out. With some simulation examples, the performance of the algorithm, in the cases of slow and fast variations of a parameter, is compared with the modified Dasgupta-Huang optimal bounding ellipsoid algorithm. These examples show the performance of the proposed algorithm.
IEE Proceedings: Vision, Image and Signal Processing (1350245X)147(3)pp. 231-237
It is shown that two algorithms obtained by simplifying a Kalman filter considered for a second-order Markov model are H∞ suboptimal. Similar to least mean squares (LMS) and normalized LMS (NLMS) algorithms, these second order algorithms can be thought of as approximate solutions to stochastic or deterministic least squares minimization. It is proved that second-order LMS and NLMS are exact solutions causing the maximum energy gain from the disturbances to the predicted and filtered errors to be less than one, respectively. These algorithms are implemented in two steps. Operation of the first step is like conventional LMS/NLMS algorithms and the second step consists of the estimation of the weight increment vector and prediction of weights for the next iteration. This step applies simple smoothing on the increment of the estimated weights to estimate the speed of the weights. Also they are cost-effective, robust and attractive for improving the tracking performance of smoothly time-varying models.
