In this paper the problem of ranging between two nodes in a wireless network of acoustic sensors in underwater medium is addressed. One of the fundamental issues in underwater wireless sensor networks (UWSNs) is to determine the location of sensors which is mostly performed by utilizing the range estimation between the unknown and anchor nodes. In general, ranging is performed via measuring either time of arrival (ToA) of the signal or received signal strength (RSS). Previous studies dealt with determining sensors' location in underwater environment with the assumption of straight line wave propagation. However, the fact is that acoustic waves travel over curved path due to the inhomogeneity of the underwater medium. In this paper, we develop a new RSS based ranging algorithm in UWSN. To do that, we first obtain an analytical relation which defines transmission loss of acoustic waves in inhomogeneous medium and, then, based on this relation a new algorithm is proposed for estimating the range between two sensors. The performance of our proposed ranging algorithm is evaluated via computer simulations and is compared with that of the conventional straight line wave propagation model. The superiority of our approach in comparison with ToA based ranging algorithms is its applicability in asynchronous scenario.

Transmission of information in nano-networks can be implemented by the emission and reception of molecules in diffusion-based molecular communications. Since the propagation environment is shared among multiple nodes, the molecules emitted by different transmitters cause interuser interference (IUI). In this paper, we propose molecular code division multiple access (MCDMA) to overcome the IUI caused by transmitted signals from asynchronous users. Based on adopting on-off keying (OOK) modulation scheme in implementing molecular codes, we develop a chip detection scheme with an adaptable threshold to decide between “on” and “off” hypotheses, by counting the number of received molecules. To eliminate the inter-symbol interference (ISI) caused by residual molecules, the receiver is modified by exploiting previously detected chips. The performance evaluation of the proposed MCDMA system via computer simulations show that the symbol error rate (SER) of the receiver considering previously detected chip outperforms the memory-less receiver. Results indicate that the MCDMA system is a promising approach to reduce interference created by asynchronous users in multi-user molecular communications.

Target localization is one of the momentous research subjects in wireless sensor networks (WSNs). Several methods have been initiated so far for the aim of target (e.g. sniper) localization using the acoustic signals produced, such as muzzle blast and shock wave in WSNs. One of the preeminent available methods is maximum likelihood estimation (MLE) algorithm, using time difference of arrival (TDOA) of the target signal received at sensor nodes. Although the MLE algorithm is asymptotically optimum and obtains high level of accuracy in comparison with other methods, nevertheless, using MLE has two major challenges. Firstly, the crucial need of this method to begin with a proper initial guess, and secondly, the possibility of not converging to a global minimum. Moreover, employing WSNs constrains the amount of power consumption that is practically possible. In this paper, to overcome the aforementioned obstacles, a two-step algorithm is proposed which in first step, a fast spherical interpolation (SI) method is utilized to prepare an appropriate initial guess for the MLE algorithm. In the second step, a clustering-based network is described to attain less power consumption across the WSN. Furthermore, to increase the probability of convergence, a cooperative incremental cluster-based estimation strategy is proposed. In addition, major issues that can affect the performance of the proposed method are investigated. Simulation results prove the capability of this method and support the claims.

In this paper, a new algorithm is proposed for cooperative beamforming and power allocation (CBPA) in the downlink of multi-input multi-output (MIMO) cognitive radio systems. Increasing the efficiency of spectrum usage is the main goal of the cognitive radio systems by servicing a number of secondary users in addition to the primary user. The proposed CBPA algorithm is developed based on a criterion in which the total signal to interference plus noise ratio (SINR) of the secondary users is maximized subject to guarantee the primary user's quality of service and a maximum transmit power. In addition, the CBPA algorithm provides the same quality of service for all secondary users. The transmitter and receiver beamforming vectors along with allocated powers to users are estimated by the CBPA algorithm in an iterative manner. The performance of the CBPA algorithm is evaluated by computer simulations and compared with that of the uplink-downlink duality method.

Co-space interference (CSI) mitigation is one of the main challenging issues in multiple input multiple output (MIMO) systems. Using beamformer in both transmit and receive sides is an approach to mitigate the CSI. In the MIMO systems employing orthogonal frequency division multiplexing (OFDM) technique, frequency domain or time domain beamforming can be used. In this paper, we propose a new time domain broadband beamforming (TBBF) for MIMO-OFDM systems based on a SVD type of block circular channel matrix. TBBF is attained by polynomial eigen-beamformer when the right and the left polynomial eigenvectors are utilized at the transmitter and the receiver sides, respectively. The new proposed TBBF is able to completely mitigate the CSI due to shaping the multiple beams in orthogonal directions. Performance of the proposed polynomial eigen-based TBBF is evaluated by computer simulations and compared with the previously proposed TBBF scheme based on the SBR2 algorithm. The results show that the proposed time domain beamforming method outperforms the SBR2-based scheme.

In this paper, a new receiver for downlink multicarrier code-division multiple-access (MC-CDMA) system is proposed when the channel between the transmitter and each user is multiple-input multiple-output (MIMO). The structure of the proposed receiver is based on the singular value decomposition (SVD) method. The conventional SVD method is able to eliminate the co-space interference (CSI) in single user scenario, however, due to multi-channel environment, it is not able to eliminate the co-space interferences caused by the transmitted signals to the other users that is called multiuser interference (MUI). By combining the SVD and space frequency spreading code, a receiver called total interference cancellation (TIC) is developed that completely eliminates both the CSI and the MUI. The performance of the TIC suffers from small singular values of the subchannel matrices. To improve the performance, a symbol-chip level MMSE receiver is developed in which joint equalization and beamforming is utilized at chip level when the MMSE criterion is employed at symbol level (after despreading). The performance of the proposed receiver is evaluated and compared under different scenarios by computer simulations. The results show that there is no error floor in the bit error rate (BER) performances of the SVD based receiver under both criteria in the multiuser environment. Compared with the TIC criterion, the receiver designed based on the symbol-chip level MMSE criterion significantly improves the BER performance.

A novel discrete tracking algorithm for mobile user based on velocity estimation in microcellular urban environment is proposed in this paper. By partitioning the mobile path and defining each partition as a state, the proposed algorithm tracks the mobile user based on hidden Markov model (HMM). The short-time Fourier analysis of the received signal strength is employed to estimate the mobile speed. Based on averaged received power (local mean), pathless, velocity estimation and using dynamic programming technique, the proposed algorithm predicts the next state of the mobile user. The proposed tracking algorithm shows good ability to predict user mobility behavior in urban area with multipath fading and shadowing phenomena.

Singular value decomposition (SVD) is a useful technique to mitigate co-space interference (CSI) in multiple input multiple output (MIMO) systems employing orthogonal frequency division multiplexing (OFDM) scheme. The SVD based frequency-domain broadband beamforming (FBBF) approach uses the beamforming matrices in both transmitter and receiver sides for each subchannel matrix. However, when the MIMO- OFDM system has a large number of subchannels, the FBBF technique is very complex due to computing the SVD of each subchannel separately. In this paper, we propose a new approach to eliminate CSI and intersymbol interference (ISI) in time- domain and frequency-domain, respectively. By the use of polynomial matrix SVD based on SBR2 algorithm, the MIMO- OFDM system is decomposed to the parallel decoupled single input single output (SISO) OFDM systems. In this way, the CSI is eliminated by time-domain broadband beamforming (TBBF) in both transmitter and receiver sides and ISI is mitigated in each SISO-OFDM system by frequency-domain equalization. The performance of the proposed method is evaluated by computer simulations for fixed broadband wireless communication system developed based on the WiMax. Simulation results show that the proposed method achieves good performance in concerned SNR interval.

Multiple-input multiple-output (MIMO) system using orthogonal frequency division multiplexing (OFDM) technique has become a promising method for reliable high data-rate wireless transmission system in which the channel is dispersive in both time and frequency domains. Due to multiple cochannel interferences in a MIMO system, the accuracy of channel estimation is a vital factor for proper receiver design in order to realize the full potential performance of the MIMO-OFDM system. A robust and improved channel estimation algorithm is proposed in this paper for MIMO-OFDM systems based on the least squares (LS) algorithm. The proposed algorithm, called improved LS (ILS), employs the noise correlation in order to reduce the variance of the LS estimation error by estimating and suppressing the noise in signal subspace. The performance of the ILS channel estimation algorithm is robust to the number of antennas in transmit and receive sides. The new algorithm attains a significant improvement in performance in comparison with that of the regular LS estimator. Also, with respect to mean square error criterion and without using channel statistics, the ILS algorithm achieves a performance very close to that of the minimum mean square error (MMSE) estimator in terms of the parameters used in practical MIMO-OFDM systems. A modification of the ILS algorithm, called modified ILS (MILS), is proposed based on using the second order statistical parameters of channel. Analytically, it is shown that the MILS estimator achieves the exact performance of the MMSE estimator. Due to no specific data sequences being required to perform the estimation, in addition to the training mode, the proposed channel estimation algorithms can also be extended and used in the tracking mode with decision-aided method.

Maximizing signal-to-interference plus noise ratio (SINR) at the output of beamformer significantly improves the performance of the receiver using an antenna array. In this paper, we propose a recursive maximum SINR (RMSINR) blind beamforming algorithm for coded-division multiple access (CDMA) systems. First, a blind beamformer algorithm called direct maximum SINR (DMSINR) that maximizes the SINR directly is introduced. Then, to reduce the computational complexity of the DMSINR algorithm, the RMSINR algorithm is developed, which achieves the maximum of SINR under a linear constraint in a recursive manner. In addition to a significant complexity reduction, as simulation results show, the RMSINR algorithm attains a very fast convergence rate with the high ability of tracking direction of arrival in time varying environments.

Channel estimation accuracy has a significant impact on the performance of multiple-input multiple-output (MIMO) wireless communication systems due to cochannel interferences in MIMO channels. An improved channel estimation algorithm is presented in this paper for MIMO systems using orthogonal frequency division multiplexing (OFDM) technique based on the least squares (LS) algorithm. The proposed algorithm employs the noise correlation in order to improve the LS estimator by estimating and suppressing the noise in signal subspace. Without knowledge of the statistical parameters of channel, the new proposed algorithm attains a significant improvement in performance in comparison with that of the regular LS estimator and is also robust to the number of antennas in transmit and receive sides.

An iterative algorithm is proposed in this paper to estimate the singular value decomposition (SVD) of multiple-input multiple-output (MIMO) channel from the received signal. The proposed algorithm is based on the constrained minimum mean-square error (MMSE) criterion. For different numbers of transmitter and receiver antennas, simulation results show that the iterative algorithm achieves good performance with respect to the SVD estimation of the MIMO channel matrix.

A new method is proposed in this paper to design the time-domain equalizer for orthogonal frequency division multiplexing systems based on maximizing signal-to-interference-plus-noise ratio (SINR) at the output of the equalizer. The method called Maximum SINR Time-domain Equalization (MSINR-TEQ) is derived based on a new formulation of SINR. Computer simulation and analytical results show the performance of the MSINR-TEQ method and its superiority in comparison with the performance of the much-used method of channel impulse response shortening.

A novel time domain equalizer (TEQ) design approach is proposed in this paper, based on maximizing the signal-to interference plus noise ratio (SINR). To increase the bandwidth efficiency of OFDM systems, the TEQ is used to mitigate the intersymbol interference (ISI) created by a channel with longer impulse response duration than that of the cyclic prefix (CP). The true ISI, ICI and noise parts of the signal at the output of the equalizer are formulated, based on the overall impulse response (OIR) (convolution of channel impulse response and equalizer impulse response). The SINR maximizing time-domain equalization (SMTE) method estimates the tap coefficients of the equalizer by maximizing the SINR of the signal at the output of the equalizer. Computer simulation and analytical results show that the performance of the SMTE method is superior to the performance of the much-used method of channel impulse response shortening (IRS).

Location estimation of mobile users has been required in cellular networks for E-911 callers as a mandatory service. In this paper, radial basis function (RBF) neural networks with a hierarchical structure are proposed to estimate the mobile user location. At each level of hierarchical structure, the coverage area of base station is divided to different cells whose areas have overlaps. The RBF network of each cell is trained based on the received signals by an antenna array from the coverage area of that cell. The trained network estimates the mobile user location based on the power and arrival direction of the received signal. Mobile location estimation is improved by zooming in the smaller cell at each level of the hierarchical structure. Simulation results show that the proposed estimation method achieves a good performance with robustness in non-line of sight propagation and urban environments.

In orthogonal frequency division multiplexing (OFDM) system, a cyclic prefix (CP) as redundant information is appended to each OFDM symbol in order to mitigate the impact of intersymbol interference (ISI) on data transmission over multipath channels. On the one hand, the CP interval should not be shorter than the channel impulse response (CIR) length in order to eliminate the ISI completely and on the other hand, appending the CP decreases the bandwidth efficiency of the OFDM systems. In this paper a new TEQ design method based on maximizing signal-to-interference plus noise ratio (SINR) is proposed in order to equalize the received signal partially and eliminate a potion of the ISI when the appended CP interval is shorter than the CIR length. The true SINR for the OFDM system is formulated based on a relative delay, D, between the starting point and the effective point of overall impulse response (OIR) (convolution of the CIR and the equalizer impulse response). The proposed method called maximum SINR time-domain equalization (MSINR-TEQ) estimates the tap coefficients of the equalizer by maximizing the SINR at the output of the equalizer based on the value of the relative delay, D. The MSINR performance is evaluated for data transmission over multipath fading channels with additive white Gaussian noise. Computer simulations show that when the CIR interval is longer than the CP interval, the TEQ designed based on the MSINR method improves the performance of the OFDM system significantly in comparison with the system without an equalizer.

The idea of adaptive state allocation (ASA) algorithm is used in this paper to substantially reduce the computational complexity of the maximum-likelihood sequence detection and estimation (MLSD/MLSDE) receiver without a significant degradation in its performance. In the ASA algorithm, the total number of states assigned to the trellis and the number of states selected from the entire set are changed adaptively based on the short-term power of the channel impulse response (CIR) or its estimate. The ASA algorithm is a combination of two methods: adaptive threshold (AT) and adaptive state partitioning (AP). In the AT method, a threshold value is formulated based on the probability of removing the correct state in the trellis diagram. At each time, only the paths whose costs are less than the minimum cost (corresponding to the best survivor path) plus the threshold value are retained and are extended to the next trellis stage. The AT method significantly reduces the computational complexity of the regular MLSDE mostly at high signal-to-noise ratio (SNR) with a negligible loss in performance. Simulation results for fading channels show that the AT method typically selects one trellis state (the minimum possible number of states) at high SNRs. In the AP method, the branch metrics are fused and diffused adaptively by using the Kullback-Leibler (KL) distance metric invoked for quantifying the differences between the probability density functions of the correct and incorrect branch metrics in the trellis. The adaptation is done such that the channel coefficients with short-term power less than a threshold are assumed to be zero in computing the branch metrics. The AP method decreases the computational complexity of the regular MLSDE at low SNRs.

Oversampled blind maximum likelihood sequence detection and estimation (MLSDE) is proposed to improve the performance and robustness of the MLSDE receiver. The performance of the MLSDE receiver depends firmly on the assumed channel model and available knowledge about the channel parameters at the receiver. A deterministic channel model is used to enhance the robustness of the receiver in a dynamic environment such as mobile communications. Oversampled joint channel estimation and data detection is proposed to improve the receiver performance which approaches the performance of the receiver knowing the channel parameters completely. The estimation part of the oversampled MLSDE leads to an RLS-type algorithm for additive colored Gaussian noise which becomes the noise model in the oversampling method. Bandwidth efficiency is achieved by using blind channel estimation (i.e. no training sequence) and two-level differentially encoded quadrature phase shift keying modulation scheme with 16 points constellation (16-DQPSK).

This paper develops a threshold method called adaptive T-algorithm to reduce the number of states in the maximum likelihood sequence detection and estimation (MLSD/MLSDE) receivers. This algorithm removes the states whose costs are larger than the minimum cost (the cost of the best survivor path) plus a threshold at each time. The threshold value is formulated based on the probability of removing the correct state in the trellis diagram and it is a function of short-term power of the channel impulse response and the variance of the additive white Gaussian noise. Simulation results for frequency flat and selective fading channels show that the adaptive T-algorithm greatly reduces the computational complexity of the MLSD/MLSDE receivers especially at high SNR with negligible loss in their performances.

The theory of adaptive sequence detection incorporating estimation of channel and related parameters is studied in the context of maximum-likelihood (ML) principles in a general framework based on the expectation and maximization (EM) algorithm. A generalized ML sequence detection and estimation (GMLSDE) criterion is derived based on the EM approach, and it is shown how the per-survivor processing and per-branch processing methods emerge naturally from GMLSDE. GMLSDE is developed into a real time detection/estimation algorithm using the online EM algorithm with coupling between estimation and detection. By utilizing Titterington's (1984) stochastic approximation approach, different adaptive ML sequence detection and estimation (MLSDE) algorithms are formulated in a unified manner for different channel models and for different amounts of channel knowledge available at the receiver. Computer simulation results are presented for differentially encoded quadrature phase-shift keying in frequency flat and selective fading channels, and comparisons are made among the performances of the various adaptive MLSDE algorithms derived earlier.

Recursive (online) expectation-maximization (EM) algorithm along with stochastic approximation is employed in this paper to estimate unknown time-invariant/variant parameters. The impulse response of a linear system (channel) is modeled as an unknown deterministic vector/process and as a Gaussian vector/process with unknown stochastic characteristics. Using these models which are embedded in white or colored Gaussian noise, different types of recursive least squares (RLS), Kalman filtering and smoothing and combined RLS and Kalman-type algorithms are derived directly from the recursive EM algorithm. The estimation of unknown parameters also generates new recursive algorithms for situations, such as additive colored noise modeled by an autoregressive process. The recursive EM algorithm is shown as a powerful tool which unifies the derivations of many adaptive estimation methods.