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This paper provides the parallel distributed Kalman filter (PDKF) with non-aligned time indexes, which uses four processing nodes to run the linear Kalman filter in parallel. To provide outputs that are in sync with the time indexes that are now in effect, each processing node aggregates prior time indexes utilizing non-aligned time indexes. This method increases system efficiency by enabling simultaneous data sharing among all nodes and parallel processing with earlier time indexes. Moreover, the consistent acknowledgment of each block's processing time by all nodes ensures smooth functioning. With the help of the suggested PDKF, computing-intensive processes can be carried out in a distributed fashion, improving computation efficiency without sacrificing estimation accuracy. Simulation results show that while achieving nearly equivalent convergence performance to the sequentially operated Kalman filter, the proposed PDKF architecture outperforms both the sequential recursive least squares (RLS) and parallel distributed adaptive signal processing RLS algorithms. This study highlights the algorithm's strong overall performance through a thorough discussion, revealing its capacity to lower computing costs without sacrificing estimation accuracy inside the network. The book delves deeper into the algorithm's overall performance, emphasizing its effectiveness beyond simple estimating accuracy. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) communication systems have a huge potential both in terms of data rate and energy efficiency, although channel estimation becomes challenging for a large number of antennas. Using a physical model allows to ease the problem by injecting a priori information based on the physics of propagation. However, such a model rests on simplifying assumptions and requires to know precisely the configuration of the system, which is unrealistic in practice. In this paper we present mpNet, an unfolded neural network specifically designed for massive MIMO channel estimation. It is trained online in an unsupervised way. Moreover, mpNet is computationally efficient and automatically adapts its depth to the signal-to-noise ratio (SNR). The method we propose adds flexibility to physical channel models by allowing a base station (BS) to automatically correct its channel estimation algorithm based on incoming data, without the need for a separate offline training phase. It is applied to realistic millimeter wave channels and shows great performance, achieving a channel estimation error almost as low as one would get with a perfectly calibrated system. It also allows incident detection and automatic correction, making the BS resilient and able to automatically adapt to changes in its environment. [ABSTRACT FROM AUTHOR]

Receivers with one-bit analog-to-digital (ADC) converters are considered as a potential solution to reduce the power consumption and hardware complexity for millimeter wave (mmWave) multiple input multiple output systems. However, it is challenging to estimate the mmWave channel with one-bit ADCs because of significant distortions of received signal and a limited scattering nature. In this work, by exploiting the sparse and low-rank properties, we develop a mmWave channel estimation algorithm which can maximize the log-likelihoods under the rank and entry-wise infinity-norm constraints. Instead of using the convex relaxation for rank operation, matrix factorization is introduced to represent the exact rank constraint implicitly. The entry-wise infinity-norm is reformulated via the log-barrier method. The optimum estimation can be obtained iteratively via projected gradient ascent algorithm. Moreover, an upper bound performance of one-bit channel estimation is derived. Simulation results demonstrate that the proposed algorithm can obtain accurate estimation with relatively low computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, a low communication parallel distributed adaptive signal processing (LC-PDASP) architecture for a group of computationally incapable and inexpensive small platforms is introduced. The proposed architecture is capable of running computationally high adaptive filtering algorithms parallely with minimally low communication overhead. A recursive least square (RLS) adaptive algorithm based on the application of multiple-input multiple-output (MIMO) channel estimation is implemented on the proposed LC-PDASP architecture. Complexity and Communication burden of proposed LC-PDASP architecture are compared with that of conventional PDASP architecture. The comparative analysis shows that the proposed LC-PDASP architecture exhibits low computational complexity and provides an improvement more than of 85% reduced communication burden than the conventional PDASP architecture. Moreover, the proposed LC-PDASP architecture provides fast convergence performance in terms of mean square error (MSE) than the PDASP architecture. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) radar systems are popular for their ability to mitigate the target radar cross section fluctuations and null Doppler. However, MIMO radar fundamentally suffers from a notable signal-to-noise ratio (SNR) loss compared to an ideal phased-array (PA) radar. In order to overcome this drawback, a distributed MIMO radar scheme with joint optimal transmit and receive signal combining method is proposed in this work. In view of the optimality, the proposed scheme outperforms the most popular combining methods of MIMO radar addressed in the literature. Also, the proposed scheme performs same as the ideal PA radar in case of the nonfluctuating target. In case of fluctuating targets, the proposed scheme outperforms PA radar at high SNR. [ABSTRACT FROM AUTHOR]

A robust recursive least square (RRLS) algorithm that has been designed for SISO communications by Bhotto and Antoniou (IEEE Signal Process Lett 18(3):185–188, 2011) is unable to work for MIMO system because of single error constraint. In this paper, a modified version of RRLS algorithm with the application of MIMO channel estimation is introduced. An RRLS algorithm is modified in such a way that it can provide much faster convergence rate in MIMO channel estimation than that of RLS and variable forgetting factor RLS (VFF-RLS) algorithms. Moreover, the optimum forgetting factor is derived for MIMO RRLS in order to minimize the error function. Simulation results show that the MIMO RRLS provides fast convergence performance as compared to RLS and VFF-RLS algorithms without additional multiplication complexity, however, an additional linear term of N in the addition complexity of modified MIMO RRLS does not enhance its computational complexity and keeps it almost equivalent to those of RLS and VFF-RLS algorithms. Moreover, it is observed that the optimum forgetting factor is highly dependent on RRLS scaling parameter, E b / N o , doppler shift and number of antennas. [ABSTRACT FROM AUTHOR]

The problem of multiple-input multiple-output (MIMO) channel estimation at millimeter-wave frequencies, both in single-user setting and in multi-user setting, is tackled in this paper. Using a subspace approach, we develop a protocol enabling the estimation of the right (respectively, left) singular vectors at the transmitter (respectively, receiver) side; then, we adapt the projection approximation subspace tracking with deflation and the orthogonal Oja algorithms to our framework and obtain two-channel estimation algorithms. We also present an alternative algorithm based on the least squares approach. The hybrid analog/digital nature of the beamformer is also explicitly taken into account at the algorithm design stage. In order to limit the system complexity, a fixed analog beamformer is used at both sides of the communication links. The obtained numerical results, showing the accuracy in the estimation of the channel matrix dominant singular vectors, the system achievable spectral efficiency, and the system bit error rate, prove that the proposed algorithms are effective and that they compare favorably, in terms of the performance-complexity tradeoff, with respect to several competing alternatives. [ABSTRACT FROM AUTHOR]

SUMMARY Particle filters have been successfully employed to track MIMO flat fading channels for wireless communications. However, an optimal importance density cannot be always found to optimize the performance of a particle filter. A suboptimal importance density such as the prior distribution can be used to reduce the complexity of the particle filtering; however, it has a problem of ignoring the current observations. A class of suboptimal particle filters uses the prior distribution as the important density and moves the predicted particles to the low-error region. In addition, particle filters require knowledge of noise processes to estimate the posterior distribution. This paper presents a suboptimal particle filter that overcomes the drawbacks of the prior importance density and the noise uncertainty by utilizing the swarm behavior in the particle propagation. The presented method will be applied to estimate the channel state information and detect the transmitted symbols of a MIMO wireless communication system under Rayleigh flat fading channel. Computer simulation of a 2 ×2 MIMO system is presented to illustrate the performance of the proposed suboptimal particle filter technique. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In sensor networks and communication systems, one is often confronted with sampling multiple sparse signals having a common support set. Multipath channels in a multiple-input multiple-output (MIMO) wireless communication setting is an interesting example where one generally needs to perform channel estimation for each transmit-receive antenna pair. MIMO multipath channels are usually (approximately) sparse and satisfy the common-support property whenever the distances between the antennas are small compared to the distance the electromagnetic wave can travel in the time corresponding to the inverse bandwidth of the communication system. This assumption is satisfied by small and medium bandwidth communication systems like OFDM and CDMA. [ABSTRACT FROM PUBLISHER]

This paper proposes a training-based channel estimation scheme for achieving quality-of-service discrimination between legitimate and unauthorized receivers in wireless multiple-input multiple-output (MIMO) channels. The proposed method has applications ranging from user discrimination in wireless TV broadcast systems to the prevention of eavesdropping in secret communications. By considering a wireless MIMO system that consists of a multiple-antenna transmitter, a legitimate receiver (LR) and an unauthorized receiver (UR), we propose a multi-stage training-based discriminatory channel estimation (DCE) scheme that aims to optimize the channel estimation performance of the LR while limiting the channel estimation performance of the UR. The key idea is to exploit the channel estimate fed back from the LR at the beginning of each stage to enable the judicious use of artificial noise (AN) in the training signal. Specifically, with knowledge of the LR's channel, AN can be properly superimposed with the training data to degrade the UR's channel without causing strong interference on the LR. The channel estimation performance of the LR in earlier stages may not be satisfactory due to the inaccuracy of the channel estimate and constraints on the UR's estimation performance, but can improve rapidly in later stages as the quality of channel estimate improves. The training data power and AN power are optimally allocated by minimizing the normalized mean-square error (NMSE) of the LR subject to a lower limit constraint on the NMSE of the UR. The proposed DCE scheme is then extended to the case with multiple LRs and multiple URs. Simulation results are presented to demonstrate the effectiveness of the proposed DCE scheme. [ABSTRACT FROM AUTHOR]

A new affine precoding and decoding method for multiple-input multiple-output (MIMO) frequency-selective fading channels is proposed. The optimal design of the affine precoder consists of a linear precoder and a training sequence, which is superimposed on the linearly precoded data in an orthogonal manner. The channel is estimated independently of the data, while the training sequence is completely nulled out in the source data detection. Furthermore, the optimal power allocation between the data and training signals is also analytically derived.. Simulation results show that the proposed method is significantly better than other affine precoding methods with regard to source detection performance and computational complexity. [ABSTRACT FROM AUTHOR]

During the initial development of the IEEE 802.11n (11n) amendment for improving the throughput of wireless LANs, a lot of excitement existed surrounding the potential higher throughput (i.e., faster downloads), and increased range (distance) achievable. However, delays in the development of this standard (which began in 2003, and is still in the final draft stages) as well as vendor, customer reluctance to adopt the pre-11n offerings in the marketplace, have generally slowed interest in this next-generation technology. However, there is still much to be excited about. The latest draft of IEEE 802.11n (Draft 3.0) offers the potential of throughputs beyond 200 Mbps, based on physical layer (PHY) data rates up to 600 Mbps. This is achieved through the use of multiple transmit and receive antennas, referred to as MIMO (multiple input, multiple output). Using techniques such as spatial division multiplexing (SDM), transmitter beamforming, and space-time block coding (STBC), MIMO is used to increase dramatically throughput over single antenna systems (by two to four times) or to improve range of reception, depending on the environment. This article offers an exposition on the techniques used in IEEE 802.11n to achieve the above improvements to throughput and range. First, the current generation WLAN devices (11a/b/g) are described in terms of the benefits offered to end users. Next, the evolution of the Tin amendment is discussed, describing the main proposals given, and illustrating reasons for the delay in standardization. Then, the changes to the PHY for 11n are presented. A description of channel modeling with MIMO is shown, followed by the signal processing techniques employed, including MIMO channel estimation and detection, space-time block coding (STBC), and transmitter beamforming. Simulation results are presented which illustrate the benefits of these techniques, versus the existing a/g structures, for both throughput and range. Finally, a brief section outlini- ng considerations for the rapid prototyping of a baseband design based on the 802.11n PHY is presented. We conclude with a discussion of the future for 11n, describing the issues addressed with Drafts 2.0 and 3.0, as well as its place in a wireless market with WiMAX and Bluetooth. [ABSTRACT FROM PUBLISHER]

This paper investigates the advantages of adaptive waveform amplitude design for estimating parameters of an unknown channel/medium under average energy constraints. We present a statistical framework for sequential design (e.g., design of waveforms in adaptive sensing) of experiments that improves parameter estimation (e.g., unknown channel parameters) performance in terms of reduction in mean-squared error (MSE). We treat an N time step design problem for a linear Gaussian model where the shape of the N input design vectors (one per time step) remains constant and their amplitudes are chosen as a function of past measurements to minimize MSE. For N=2, we derive the optimal energy allocation at the second step as a function of the first measurement. Our adaptive two-step strategy yields an MSE improvement of at least 1.65 dB relative to the optimal nonadaptive strategy, but is not implementable since it requires knowledge of the noise amplitude. We then present an implementable design for the two-step strategy which asymptotically achieves optimal performance. Motivated by the optimal two-step strategy, we propose a suboptimal adaptive N-step energy allocation strategy that can achieve an MSE improvement of more than 5 dB for N=50. We demonstrate our general approach in the context of MIMO channel estimation and inverse scattering problems [ABSTRACT FROM PUBLISHER]

In this paper, a new computationally simple approach to blind decoding of orthogonal space-time block codes (OSTBCs) is proposed. Using specific properties of OSTBCs, the authors' approach estimates the channel matrix in a closed form and in a fully blind fashion. This channel estimate is then used in the maximum-likelihood (ML) receiver to decode the information symbols. The proposed estimation technique provides consistent channel estimates, and, as a result, the performance of the authors' blind ML receiver approaches that of the coherent ML receiver, which exploits the exact channel state information (CSI). Simulation results demonstrate the performance improvements achieved by the proposed blind decoding algorithm relative to the popular differential space-time modulation scheme. [ABSTRACT FROM AUTHOR]

Large‐scale multiple‐input multiple‐output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large‐scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, a spectrum‐efficient superimposed pilot design for downlink large‐scale MIMO scenarios is proposed, where frequency‐domain pilots of different transmit antennas occupy completely the same subcarriers in the frequency domain. Meanwhile, spatial–temporal common sparsity of large‐scale MIMO channels motivates us to exploit the emerging theory of structured compressive sensing (CS) for reliable MIMO channel estimation, which is realised by the proposed structured subspace pursuit (SSP) algorithm to simultaneously recover multiple channels with low pilot overhead. Simulation results demonstrate that the proposed scheme performs well and can approach the performance bound. [ABSTRACT FROM AUTHOR]

In this article, the intended purpose is to show an innovative technique for estimating the MIMO channel at millimeter wave bands, candidates for mobile 5G technology, by using hardware acceleration, game engines and heuristic algorithms applied to optical ray launching techniques. To verify the performance of the ray launching tool, the normalized Power Delay Profile (PDP) was simulated. The channel was analyzed using the mean square delay error (RMS), the average value of the delay (MD) and the basic propagation loss (PL). The results obtained in computational precision and time were compared with those of a traditional ray tracing tool simulation programmed in MATLAB and with the measurements made in the 57 to 66 GHz range in a specialized laboratory. The results show that the presented technique becomes efficiently profitable from a small number of simulated events (reflections, diffractions). [ABSTRACT FROM AUTHOR]

A novel approach is proposed for correlated multiple-input multiple-output (MIMO) channel estimation based on reduced-rank (RR) technique and partial channel state information (CSI). In contrast to previous proposals that used the channel correlation matrix (CCM) and its eigendecomposition, this paper shows that close linear minimum mean-square-error (LMMSE) performance can be achieved with the use of predefined bases derived from the knowledge of the maximum angular dispersion. A theoretical framework to synthesize a suitable set of bases is provided, from which discrete prolate spheroidal sequences (DPSSs) are identified as one of the appropriate predefined bases for spatial channel representation. The robustness of the proposed estimator allows changes in the propagation scenario to be managed according to the demands of realistic communications systems. The performance analysis of the channel estimator is shown and corroborated with simulation results. [ABSTRACT FROM PUBLISHER]