Reconfigurable holographic surfaces (RHS) are intrinsically amalgamated with reconfigurable intelligent surfaces (RIS), for beneficially ameliorating the signal propagation environment. This potent architecture significantly improves the system performance in non-line-of-sight scenarios at a low power consumption. Briefly, the RHS technology integrates ultra-thin, lightweight antennas onto the transceiver, for creating sharp, high-gain directional beams. We formulate a user sum-rate maximization problem for our RHS-RIS-based hybrid beamformer. Explicitly, we jointly design the digital, holographic, and passive beamformers for maximizing the sum-rate of all user equipment (UE). To tackle the resultant nonconvex optimization problem, we propose an alternating maximization (AM) framework for decoupling and iteratively solving the subproblems involved. Specifically, we employ the zero-forcing criterion for the digital beamformer, leverage fractional programming to determine the radiation amplitudes of the RHS and utilize the Riemannian conjugate gradient algorithm for optimizing the RIS phase shift matrix of the passive beamformer. Our simulation results demonstrate that the proposed RHS-RIS-based hybrid beamformer outperforms its conventional counterpart operating without an RIS in multi-UE scenarios. The sum-rate improvement attained ranges from 8 bps/Hz to 13 bps/Hz for various transmit powers at the base station (BS) and at the UEs, which is significant.

We consider the uplink of a Selective User-Forwarded Cell-free massive MIMO (SUF-CF-mMIMO) system, where each access point (AP) forwards the received symbols of only a selected subset of users to the centralized processing unit (CPU) for coherent combining. SUF-CF-mMIMO gives significant savings in fronthaul signaling compared to the conventional CF-mMIMO. This paper investigates the uplink performance of SUF-CF-mMIMO with one-bit quantized symbols sent over the fronthaul to the CPU by each AP. Novel expressions for spectral efficiency (SE) are derived for the uplink in the presence of quantization distortion. Simulation results show that SUF-CF-mMIMO is more robust to quantization effects compared to the conventional CF-mMIMO.

Among the approaches used to jointly estimate the directions of arrival (DOAs) of K targets in a multiple-input multiple-output dual-function radar communication system, the method based on the identification of the K largest local maxima of the modulus of the Fourier transform (FT) of the signal received by the antennas has the advantage of having a low computational cost. However, the local maxima do not necessarily correspond to the values of interest. To avoid this problem, we present an operation mode making it possible to address the estimations of the DOAs separately. To this end, we propose to design a waveform reducing the magnitudes of the signals back-scattered by the targets except the one located in a specific zone. Then, this operation mode is extended to address the case of a received signal disturbed by an additive white Gaussian noise. Finally, simulation results confirm that this approach improves the standard approach based on the FT.

The creation of the NPTEL platform in India has led to a vast population of engineering students getting access to quality online content for Signal Processing. These courses are globally accessible, free of cost, and also provide a means of obtaining certificates of proficiency by taking a proctored examination. Recently, a European Union funded project, MIELES, has supported the activity of creating online courses in the fields related to Signal Processing. This paper presents the details and experiences of creating course content and presents guidelines for prospective content creators.

A novel class of k-space trajectories for magnetic resonance imaging (MRI) sampling using space filling curves (SFCs) is presented here. More specifically, Peano, Hilbert and Sierpinski curves are used. We propose 1-shot and 4-shot variable density SFCs by utilizing the space coverage provided by SFCs in different iterations. The proposed trajectories are compared with state-of-the-art echo planar imaging (EPI) trajectories for 128 × 128 and 256 × 256 phantom and brain images. The simulation results show that the readout time is reduced by up to 45% for the 128 × 128 image with little compromise in reconstruction quality. Also, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index are improved by 2.32 dB and 0.1009, respectively, with an 18% shorter readout time using the 4-shot Hilbert SFC trajectory for reconstructing a 256 × 256 brain MRI image.

The development of compressed sensing (CS) techniques for magnetic resonance imaging (MRI) is enabling a speedup of MRI scanning. To increase the incoherence in the sampling, a random selection of points on the k-space is deployed and a continuous trajectory is obtained by solving a traveling salesman problem (TSP) through these points. A feasible trajectory satisfying the gradient constraints is then obtained by parameterizing it using state-of-the-art methods. In this paper, a constrained convex optimization based method to obtain feasible trajectories is proposed. The method is motivated by the fact that the readout time is proportional to the number of sample points and includes the lengths of the segments of the trajectory in the cost function to obtain variable length trajectories. The proposed method provides a reduction in readout time by more than 50% for random-like trajectories with an improvement of about 1.5 dB in peak signal-to-noise ratio (PSNR) and 0.0762 in structural similarity (SSIM) index on average for a realistic brain phantom MRI image adopting single-shot trajectories.

Simultaneous wireless information and power transfer (SWIPT) has gained significant popularity in the recent past owing to its applications in a wide range of use cases. Although SWIPT has been fairly well investigated in the literature, the existing work has mainly focused on attaining the optimal rate energy (RE) tradeoff assuming Gaussian input alphabet. However, practical systems operate with finite input alphabets such as quadratic-amplitude modulation (QAM)/phase-shift keying. We characterize the attainable RE tradeoff in SWIPT systems employing finite input alphabet for transmission over parallel Gaussian channels of say orthogonal frequency-division multiplexing subcarriers or multiple-input multiple-output streams. Some of the key results in the literature that assume Gaussian input alphabet are shown to be special cases of our results. Furthermore, we provide insights into our results with the aid of graphical illustrations, which throw light on the optimal power allocation policy for various energy-harvesting constraints. Furthermore, we consider practically relevant time-sharing and power-splitting schemes operating with finite input alphabet and characterize their RE tradeoff. Their optimal solutions in the asymptotic regime are obtained, which serve as low-complexity solutions suitable for practical implementation. Our simulation studies have demonstrated that the Gaussian input assumption significantly over-estimates the attainable RE tradeoff, especially when the signal set employed is small. Furthermore, it is observed through numerical simulations that the proposed optimal power allocation performs significantly better than the power allocation based on the Gaussian input assumption. Specifically, as much as 30% rate improvement is observed when employing the classic 4-QAM signal set.

Unmanned aerial vehicles (UAVs) have gained a significant popularity in the recent past owing to their easy deployability and wide range of applications. In most of the short- and medium-range applications, Wi-Fi is used as the access technology for establishing communication between the ground stations and the UAVs. Although Wi-Fi is known to perform well in most of the scenarios, it is important to note that Wi-Fi has been mainly designed for indoor communication in rich scattering environments, whereas the air-to-ground (A2G) channel is characterized by sparse scattering. Considering this important difference in the channel characteristics, we revisit some of the Wi-Fi features and propose efficient design alternatives. First, we provide a statistical model for the sparse A2G channel and design an optimal time-domain quantizer (TDQ) for its feedback. In contrast to the frequency-domain quantizer (FDQ) of the IEEE 802.11n/ac Standard, the proposed TDQ exploits the time-domain sparsity in the channel and requires about 15 times lesser quantization bits than FDQ. Second, we propose a beamforming (BF) scheme with the aid of full-diversity rotation (FDR) matrices and analytically evaluate its symbol error probability in order to quantify the attainable diversity order. Our numerical simulations demonstrate that the proposed FDR-BF scheme outperforms the relevant benchmark schemes in both coded as well as uncoded scenarios. Specifically, the proposed FDR-BF scheme was observed to attain a signal-to-noise ratio gain as high as 6dB compared with the popular geometric mean decomposition-based BF scheme, when operating at an elevation angle of 7.5°.

Joint estimation of channel and sampling frequency offset (SFO) in orthogonal frequency division multiplexing (OFDM) systems, using Bayesian framework, is shown in this study. Hybrid Cramér–Rao lower bounds (HCRLBs) for the estimation of SFO together with channel are obtained. The significance of Bayesian approach in the formulation of joint estimator is shown by comparing HCRLB with the corresponding standard CRLB. The authors propose a joint maximum a posteriori (JMAP) algorithm for the estimation of channel and SFO in OFDM, utilising the prior statistical knowledge of channel. To reduce the complexity of JMAP estimator, a modified MAP algorithm, which has no grid searches, is also proposed. Also, they analyse the effect of inaccurate knowledge of channel statistics and signal‐to‐noise ratio on the estimation accuracy. The estimation methods are analysed by numerical simulations and resultant conclusions validate the better performance of the proposed algorithms when compared with previous algorithms.

We show that certain signal constellations invoked for classic differential encoding result in a phenomenon we term as the unbounded differential constellation size (UDCS). Various existing differential transmission schemes that suffer from this issue are identified. Then, we propose an enhanced algebraic field extension-based differential spatial modulation (AFE-DSM) scheme and its enhanced counterpart that strikes a diversityrate tradeoff (AFE-DSM-DR), both of which overcome the UDCS issue without compromising its full transmit diversity advantage. Furthermore, the proposed schemes are extended to incorporate amplitude and phase shift keying (APSK) in order to exploit all the available degrees of freedom. Additionally, we propose a pair of detection schemes specially designed for APSK-aided differential transmission schemes. Explicitly, we conceive the buffered minimum mean squared error (B-MMSE) detector and buffered maximum likelihood (B-ML) detector, which exploit the knowledge of previously detected symbols in order to further improve the detection performance. Our simulation results have shown that the proposed detectors are capable of bridging the performance gap between the conventional differential detector (CDD) and the coherent detector that has full channel state information. Specifically, when employing the proposed APSKaided AFE-DSM scheme operating at a rate of 2 b per channel use, the B-MMSE and B-ML detectors are observed to give about 3and 3.5-dB signal-to-noise ratio gain with respect to their CDD counterpart at a bit error ratio of 10-5.

We consider the recovery of sparse signals that share a common support from multiple measurement vectors. The performance of several algorithms developed for this task depends on parameters like dimension of the sparse signal, dimension of measurement vector, sparsity level, measurement noise. We propose a fusion framework, where several multiple measurement vector reconstruction algorithms participate and the final signal estimate is obtained by combining the signal estimates of the participating algorithms. We present the conditions for achieving a better reconstruction performance than the participating algorithms. Numerical simulations demonstrate that our fusion algorithm often performs better than the participating algorithms.

In this letter, we quantify the transmit diversity order of the SM system operating in a closed-loop scenario. Specifically, the SM system relying on Euclidean distance based antenna subset selection (EDAS) is considered and the achievable diversity gain is evaluated. Furthermore, the resultant trade-off between the achievable diversity gain and switching gain is studied. Simulation results confirm our theoretical results. Specifically, switching gain is studied. Simulation results confirm our theoretical results. Specifically, at a symbol error rate of about 10-4the signal-to-noise ratio gain achieved by EDAS is about 7 dBat a symbol error rate of about 10-4the signal-to-noise ratio gain achieved by EDAS is about 7 dB in case of 16-QAM and about 5 dB in case of 64-QAM.

The authors consider the channel estimation problem in the context of a linear equaliser designed for a frequency selective channel, which relies on the minimum bit‐error‐ratio (MBER) optimisation framework. Previous literature has shown that the MBER‐based signal detection may outperform its minimum‐mean‐square‐error (MMSE) counterpart in the bit‐error‐ratio performance sense. In this study, they develop a framework for channel estimation by first discretising the parameter space and then posing it as a detection problem. Explicitly, the MBER cost function (CF) is derived and its performance studied, when transmitting binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals. It is demonstrated that the MBER based CF aided scheme is capable of outperforming existing MMSE, least square‐based solutions.

Joint estimation of the random impairments, phase noise (PHN) and channel, in orthogonal frequency division multiplexing (OFDM) system is investigated in this study. Bayesian Cramér‐Rao lower bounds (BCRLBs) for the joint estimation of PHN and channel are derived, and are compared with the corresponding standard CRLB, which shows the significance of joint estimator in a Bayesian framework. The authors propose maximum a posteriori algorithms for the estimation of PHN and channel, utilising their statistical knowledge which is known a priori. The performance of the estimation methods is studied through simulations and numerical results show that the performance of the proposed algorithms is better than existing algorithms and is closer to BCRLB.

A design methodology based on the Minimum Bit Error Ratio (MBER) framework is proposed for a non-regenerative Multiple-Input Multiple-Output (MIMO) relay-aided system to determine various linear parameters. We consider both the Relay-Destination (RD) as well as the Source-Relay-Destination (SRD) link design based on this MBER framework, including the precoder, the Amplify-and-Forward (AF) matrix and the equalizer matrix of our system. It has been shown in the previous literature that MBER based communication systems are capable of reducing the Bit-Error-Ratio (BER) compared to their Linear Minimum Mean Square Error (LMMSE) based counterparts. We design a novel relay-aided system using various signal constellations, ranging from QPSK to the general M-QAM and M-PSK constellations. Finally, we propose its sub-optimal versions for reducing the computational complexity imposed. Our simulation results demonstrate that the proposed scheme indeed achieves a significant BER reduction over the existing LMMSE scheme.

Spatial Modulation (SM) is a recently developed low-complexity Multiple-Input Multiple-Output scheme that jointly uses antenna indices and a conventional signal set to convey information. It has been shown that the Maximum-Likelihood (ML) detector of an SM system involves joint detection of the transmit antenna index and of the transmitted symbol, hence, the ML search complexity grows linearly with the number of transmit antennas and the size of the signal set. To circumvent the problem, we show that the ML search complexity of an SM system may be rendered independent of the constellation size, provided that the signal set employed is a square- or a rectangular-QAM. Furthermore, we derive bounds for the capacity of the SM system and derive the optimal power allocation between the data and the training sequences by maximizing the worst-case capacity bound of the SM system operating with imperfect channel state information. We show, with the aid of our simulation results, that the proposed detector is ML-optimal, despite its lowest complexity amongst the existing detectors. Furthermore, we show that employing the proposed optimal power allocation provides a substantial gain in terms of the SM system's capacity as well as signal-to-noise ratio compared to its equal-power-allocation counterpart. Finally, we compare the performance of the SM system to that of the conventional Multiple-Input Multiple-Output (MIMO) system and show that the SM system is capable of outperforming the conventional MIMO system by a significant margin, when both the systems are employing optimal power splitting.

In this paper, we propose an eigen framework for transmit beamforming for single-hop and dual-hop network models with single antenna receivers. In cases where number of receivers is not more than three, the proposed Eigen approach is vastly superior in terms of ease of implementation and computational complexity compared with the existing convex-relaxation-based approaches. The essential premise is that the precoding problems can be posed as equivalent optimization problems of searching for an optimal vector in the joint numerical range of Hermitian matrices. We show that the latter problem has two convex approximations: the first one is a semi-definite program that yields a lower bound on the solution, and the second one is a linear matrix inequality that yields an upper bound on the solution. We study the performance of the proposed and existing techniques using numerical simulations.

Numerous algorithms have been proposed recently for sparse signal recovery in Compressed Sensing (CS). In practice, the number of measurements can be very limited due to the nature of the problem and/or the underlying statistical distribution of the non-zero elements of the sparse signal may not be known a priori. It has been observed that the performance of any sparse signal recovery algorithm depends on these factors, which makes the selection of a suitable sparse recovery algorithm difficult. To take advantage in such situations, we propose to use a fusion framework using which we employ multiple sparse signal recovery algorithms and fuse their estimates to get a better estimate. Theoretical results justifying the performance improvement are shown. The efficacy of the proposed scheme is demonstrated by Monte Carlo simulations using synthetic sparse signals and ECG signals selected from MIT-BIH database.

Maximum likelihood (ML) algorithms, for the joint estimation of synchronisation impairments and channel in multiple input multiple output–orthogonal frequency division multiplexing (MIMO–OFDM) system, are investigated in this work. A system model that takes into account the effects of carrier frequency offset, sampling frequency offset, symbol timing error and channel impulse response is formulated. Cramér–Rao lower bounds for the estimation of continuous parameters are derived, which show the coupling effect among different impairments and the significance of the joint estimation. The authors propose an ML algorithm for the estimation of synchronisation impairments and channel together, using the grid search method. To reduce the complexity of the joint grid search in the ML algorithm, a modified ML (MML) algorithm with multiple one‐dimensional searches is also proposed. Further, a stage‐wise ML (SML) algorithm using existing algorithms, which estimate less number of parameters, is also proposed. Performance of the estimation algorithms is studied through numerical simulations and it is found that the proposed ML and MML algorithms exhibit better performance than SML algorithm.

We propose an eigenvalue based technique to solve the Homogeneous Quadratic Constrained Quadratic Programming problem (HQCQP) with at most three constraints which arise in many signal processing problems. Semi-Definite Relaxation (SDR) is the only known approach and is computationally intensive. We study the performance of the proposed fast eigen approach through simulations in the context of MIMO relays and show that the solution converges to the solution obtained using the SDR approach with significant reduction in complexity.

In this work, the grid mismatch problem for a single snapshot direction of arrival estimation problem is studied. We derive a Bayesian Cramer-Rao bound for the grid mismatch problem with the errors in variables model and propose a block sparse estimator for grid matching and sparse recovery.

We propose a novel method of constructing Dispersion Matrices (DM) for Coherent Space-Time Shift Keying (CSTSK) relying on arbitrary PSK signal sets by exploiting codes from division algebras. We show that classic codes from Cyclic Division Algebras (CDA) may be interpreted as DMs conceived for PSK signal sets. Hence various benefits of CDA codes such as their ability to achieve full diversity are inherited by CSTSK. We demonstrate that the proposed CDA based DMs are capable of achieving a lower symbol error ratio than the existing DMs generated using the capacity as their optimization objective function for both perfect and imperfect channel estimation.

In this letter, we propose a reduced-complexity implementation of partial interference cancellation group decoder with successive interference cancellation (PIC-GD-SIC) by employing the theory of displacement structures. The proposed algorithm exploits the block-Toeplitz structure of the effective matrix and chooses an ordering of the groups such that the zero-forcing matrices associated with the various groups are obtained through Schur recursions without any approximations. We show using an example that the proposed implementation offers a significantly reduced computational complexity compared to the direct approach without any loss in performance.

In this paper, we consider spatial modulation (SM) operating in a frequency-selective single-carrier (SC) communication scenario and propose zero-padding instead of the cyclic-prefix considered in the existing literature. We show that the zero-padded single-carrier (ZP-SC) SM system offers full multipath diversity under maximum-likelihood (ML) detection, unlike the cyclic-prefix based SM system. Furthermore, we show that the order of ML detection complexity in our proposed ZP-SC SM system is independent of the frame length and depends only on the number of multipath links between the transmitter and the receiver. Thus, we show that the zero-padding applied in the SC SM system has two advantages over the cyclic prefix: 1) achieves full multipath diversity, and 2) imposes a relatively low ML detection complexity. Furthermore, we extend the partial interference cancellation receiver (PIC-R) proposed by Guo and Xia for the detection of space-time block codes (STBCs) in order to convert the ZP-SC system into a set of narrowband subsystems experiencing flat-fading. We show that full rank STBC transmissions over these subsystems achieves full transmit, receive as well as multipath diversity for the PIC-R. Furthermore, we show that the ZP-SC SM system achieves receive and multipath diversity for the PIC-R at a detection complexity order which is the same as that of the SM system in flat-fading scenario. Our simulation results demonstrate that the symbol error ratio performance of the proposed linear receiver for the ZP-SC SM system is significantly better than that of the SM in cyclic prefix based orthogonal frequency division multiplexing as well as of the SM in the cyclic-prefixed and zero-padded single carrier systems relying on zero-forcing/minimum mean-squared error equalizer based receivers.

Differentially encoded and non-coherently detected transceivers exhibit a low complexity, since they dispense with complex channel estimation. Albeit this is achieved at the cost of requiring an increased transmit power, they are particularly beneficial, for example in cooperative communication scenarios, where the employment of channel estimation for all the mobile-to-mobile links may become unrealistic. In pursuit of high bandwidth efficiency, differential amplitude and phase shift keying (DAPSK) was devised using constellations of multiple concentric rings. In order to increase resilience against the typical high-Doppler-induced performance degradation of DAPSK and/or enhance the maximum achievable error-free transmission rate for DAPSK modulated systems, multiple-symbol differential detection (MSDD) may be invoked. However, the complexity of the maximum-a-posteriori (MAP) MSDD increases exponentially with the detection window size and hence may become excessive upon increasing the window size, especially in the context of iterative detection aided channel coded system. In order to circumvent this excessive complexity, we conceive a decomposed two-stage iterative amplitude and phase (A/P) detection framework, where the challenge of having a non-constant-modulus constellation is tackled with the aid of a specifically designed information exchange between the independent A/P detection stages, thus allowing the incorporation of reduced-complexity sphere detection (SD). Consequently, a near-MAP-MSDD performance can be achieved at a significantly reduced complexity, which may be five orders of magnitude lower than that imposed by the traditional MAP-MSDD in the 16-DAPSK scenario considered.

Motivated by the recent Coherent Space-Time Shift Keying (CSTSK) philosophy, we construct new dispersion matrices for rotationally invariant PSK signaling sets. Given a specific PSK signal constellation, the dispersion matrices of the existing CSTSK scheme were chosen by maximizing the mutual information over randomly generated sets of dispersion matrices. In this contribution we propose a general method for constructing a set of structured dispersion matrices for arbitrary PSK signaling sets using Field Extension (FE) codes and then study the attainable Symbol Error Rate (SER) performance of some example constructions. We demonstrate that the proposed dispersion scheme is capable of outperforming the existing dispersion arrangement at medium to high SNRs.

Direction Of Arrival (DOA) estimation, using a sensor array, in the presence of non-Gaussian noise using Fractional Lower-Order Moments (FLOM)matrices is studied. In this paper, a new FLOM based technique using the Fractional Lower Order Infinity Norm based Covariance (FLIC) Matrix is proposed. The bounded property and the low-rank subspace structure of the FLIC matrix is derived. Performance of FLIC based DOA estimation using MUSIC, ESPRIT, is shown to be better than other FLOM based methods.

We consider the design of a linear equalizer with a finite number of coefficients in the context of a classical linear intersymbol-interference channel with additive Gaussian noise for channel estimation. Previous literature has shown that Minimum Bit Error Rate(MBER) based detection has outperformed Minimum Mean Squared Error (MMSE) based detection. We pose the channel estimation problem as a detection problem and propose a novel algorithm to estimate the channel based on the MBER framework for BPSK signals. It is shown that the proposed algorithm reduces BER compared to an MMSE based channel estimation when used in MMSE or MBER detection.

The knowledge of Doppler spread is very important in many channel estimation (tracking) algorithms for flat fading channels. Recently, doppler spread estimators based on the profile of the power spectral density (psd) have been published. They make use of the fact that there is an abrupt change in the psd around the maximum Doppler frequency. However, this assumption is invalid in low signal-to-noise ratios (SNRs). In this paper, we introduce an iterative Kalman autoregressive (IKAR) estimator which overcomes this drawback. In doing so, we also study the excellent detection properties of the IKAR detector and demonstrate its superiority over some of the conventional detectors. The IKAR estimator performance is studied for two low SNR scenarios.

In this paper, we address the issue of perturbed information at the transmitter in a multiple-input multiple-output (MIMO) system. Two schemes, correlation matrix feedback and channel information feedback, are proposed to convey information necessary for achieving capacity to the transmitter. This information is perturbed due to various impairments. A perturbation analysis is carried out to study the variations in mutual information for each of the proposed schemes. For ergodic channels, this analysis is used to design a MIMO system with a limited rate feedback. Using a codebook-based approach, vector quantizers are designed to minimize the loss in ergodic capacity for each of the proposed schemes, and their performance is studied. A non-codebook method, parameter-based feedback, is also presented, and the underlying rate-performance tradeoff is highlighted

This paper describes a channel estimation scheme for MIMO interleaved orthogonal frequency division multiplexing (IOFDM) systems. The mean square error (MSE) of the proposed scheme is derived, and for the MSE of the channel estimates to be minimized, it is shown in this paper that the pilot tones have to be equispaced in frequency, equipowered, with the entire power being allotted to the pilot tones in one of the P data blocks of an IOFDM block, and the tones in the corresponding positions in the remaining P-1 data blocks being equal to zero. Through simulations, a comparison is made between the proposed training scheme for MIMO-IOFDM and other training sequences, and also between the proposed training scheme and the existing schemes which estimate the channel over P consecutive blocks.

Three reduced complexity equalization schemes for Zero-padded OFDM systems are described. These schemes guarantee Zero-Forcing (ZF) equalization irrespective of the channel nulls. Two of these schemes implement the minimum-norm ZF equalizer efficiently using QR decomposition. In the third scheme, the channel zeros are grouped as being inside or outside or on the unit circle. These groups are then equalized sequentially in a manner so as to tackle excess noise amplification. The three schemes are compared for their computational complexity and Bit Error Rate (BER) performance. It is shown that the attractive scheme depends on the system specifications. The BERComputations trade off occurring in the choice of the right algorithm is also highlighted.

A new orthogonal frequency division multiplexing (OFDM) system for wireless communications over frequency selective fading channels is presented. The proposed interleaved orthogonal frequency division multiplexing (IOFDM) system promises higher code rate as compared to the conventional OFDM system without bandwidth expansion and with very slight increase in computational complexity. Simulation results verify that the performance, in terms of bit error rate, of the proposed IOFDM system is very close to the conventional OFDM system.

The effect of spatial smoothing on direction of arrival (DOA) estimates obtained using weighted eigen-based state space methods/ESPRIT is analyzed. Expressions for the asymptotic mean-squared error in the estimates of the signal zeros and the DOA estimates, along with some general properties of the estimates are derived. It is shown that proper choice of the subarray length can improve the performance of the method significantly. Based on the asymptotic expressions, an optimum weighting matrix minimizing the mean-squared error in one of the DOA is derived.>

The effect of using a spatially smoothed forward-backward covariance matrix on the performance of noise subspace-based methods like MUSIC and the minimum-norm method for the direction-of-arrival (DOA) estimation problem is analyzed. In particular, asymptotic results for the mean-squared error in the estimates of the signal zeros and the DOA are derived. An important outcome of this analysis is that for MUSIC the error in the signal zeros is shown to exhibit a different trend compared to the error in the DOA estimates, leading to difficulty in interpreting the spatial spectrum. For instance, when smoothing is used, the peaks in the spatial spectrum become sharper, giving the impression of higher resolution, whereas in reality the estimates of the DOA may, in fact, have deteriorated compared with the ones obtained using minimal or no smoothing. With regard to the minimum-norm method, the errors in the signal zeros exhibit the same trend as the DOA estimates causing no such problem. The relative comparison of the methods shows that proper spatial smoothing enables the performance of the minimum-norm method to be made comparable to MUSIC.>

The authors analyze the performance of the minimum-norm method for estimating the direction of arrival (DOA) of plane waves in white noise in the case of a linear equispaced sensor array. They examine the perturbation in the roots of the polynomial formed in the intermediate step of the minimum-norm method. In particular, asymptotic results for the mean-squared error in the estimates of the direction of arrival are derived. Simple closed-form expressions are obtained for the one- and two-source case to get further insight. Computer simulation results that substantiate the analysis are provided and compared to those obtained for Root-MUSIC. It is shown that the relative performance of the two methods is directly dependent on the ratio of their parameter sensitivities.>