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Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier’s quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequency-domain (FD) correlation of the channel’s frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.

This work conceives the robust linear transceivers for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) multi-sensor network under imperfect channel state information (CSI) at the fusion center (FC). Both the popular stochastic (S-) and norm ball CSI uncertainty (N-CSIU) models are considered for robust design. The proposed techniques are based on two design criterion, the first being, minimizing the mean squared error (MSE) of the estimate at the FC subject to total network power or individual sensor power constraints. Second, minimizing the total power consumption in the network while meeting a predefined level of MSE performance. Furthermore, the framework for precoder and combiner optimization is based on results from majorization theory, which leads to non-iterative closed-form solutions for the transceivers. While the most general scenario with correlated parameters and arbitrary observation SNR is considered to begin with, scenarios with uncorrelated parameters and high observation SNR are also considered as special cases, which makes the analysis comprehensive. Simulation results are presented to demonstrate the efficacy of the proposed schemes.

This paper proposes affine-precoded superimposed pilot (SIP) design, followed by channel state information (CSI) estimation techniques for millimeter wave (mmWave) MIMO-OFDM systems. Toward this end, a multiple measurement vector (MMV) sparse Bayesian learning (SBL)-based SIP (M-SIP) technique is initially derived to exploit the simultaneous-sparsity of the beamspace channel vector across the subcarriers, thereby leading to improved performance in comparison to the existing single measurement vector (SMV)-based SBL and other techniques. This is subsequently extended to the data-aided J-SIP technique that performs joint channel estimation and data detection, and is shown to ultimately yield a better mean squared error (MSE) of the channel estimate and bit error-rate (BER). The SIP-based simultaneous-sparse channel estimation framework is extended to time-selective wideband, i.e. doubly-selective mmWave MIMO channels, via the MMV sparse Kalman filtering-based SIP (MK-SIP) technique for tracking the CSI, and subsequently also to the joint Kalman filtering-based SIP (JK-SIP) for data-aided CSI acquisition. Bayesian Cramer-Rao bounds are determined both for quasistatic and doubly-selective mmWave MIMO-OFDM channel estimation. Simulation results, which also incorporate practical channel realizations, are provided to demonstrate performance improvement considering various metrics, such as MSE and BER.

We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave hybrid MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF) circuitry during the transmission of zero symbols. Subsequently, a Bayesian learning (BL) technique is proposed for sparse channel estimation, which relies on multiple measurement vectors combined with selective subcarrier grouping for enhanced estimation. Additionally, an online BL based Kalman filter (OBL-KF) is designed for sparse channel tracking in doubly-selective mmH-MFO systems. Then the Bayesian Cramer-Rao lower bounds (BCRLBs) are derived for characterizing the performance of the proposed frequency-selective and doubly-selective channel estimation techniques. Finally, a limited feedback based algorithm relying on beamspace channel estimates is proposed for hybrid precoder/combiner design. The accuracy of our analytical results is confirmed by our simulation results.

Sparse Bayesian learning (SBL)-based approximately sparse channel estimation schemes are conceived for space-time trellis coded (STTC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems relying on trellis-based encoding and decoding over the data subcarriers. First, a pilot-aided channel estimation scheme is developed employing the multiple response extension of SBL (MSBL) framework. Subsequently, a novel data-aided joint channel estimation and data decoding framework relying on optimal maximum likelihood sequence detection (MLSD) is intrinsically amalgamated with our powerful EM-based MSBL algorithm. Explicitly, an MSBL-based MIMO channel estimate is gleaned in the E-step followed by a novel modified path-metric- based Viterbi decoder in the M-step. Our theoretical analysis characterizes the performance of the proposed schemes in terms of the associated frame error rate (FER) upper bounds by explicitly considering the effect of estimation errors along with evaluating the product measure of the STTC under consideration. Finally, our simulation results are complemented by the Bayesian Cramér-Rao bound (BCRB), the associated complexity analysis and the performance of the proposed schemes for validating the theoretical bounds.

This work considers multiple user power-domain non-orthogonal multiple access (NOMA)-based downlink (DL) and uplink (UL) communication systems with potentially dissimilar fading links for all the users that can follow one of several possible distributions such as Rayleigh, Rician, Nakagami- $m$ , Nakagami- $q$ , $\kappa -\mu $ , $\eta -\mu $ , Nakagami-lognormal. The presented analysis, which is based on approximating the probability density function (PDF) of the channel gain as a sum of Gamma distributions, is sufficiently general and applicable in a multitude of NOMA scenarios. For both the UL and DL, closed-form expressions are determined for the outage probability at the users considering both statistical channel state information (CSI)-based as well as instantaneous CSI-based ordering techniques. Analytical expressions for the outage probability and the ensuing diversity orders have also been obtained for the NOMA DL system at high SNRs. Furthermore, similar expressions for the outage probability and outage floor for the NOMA UL system have been derived at high SNRs. Finally, simulation results have been presented to authenticate the analytical results derived and provide insights into the NOMA system performance.

This paper proposes a robust reconstruction scheme for image/ video transmission in space-time trellis coding (STTC) based MIMO wireless multimedia sensor networks (WMSN). The information bits of the image/ video stream are modulated using STTC prior to transmission over the MIMO wireless channel between the multimedia sensors and the cluster head. At the receiver, a novel total variation sequence detector (TVSD) is developed that employs an anisotropic total variation (TV) regularized cost function to leverage the bounded variation property of the image/ video stream, in addition to the coding and diversity advantages of the STTC, for improved image/ video recovery. For reconstruction, a novel bi-layered hierarchical trellis based modified Viterbi decoder is developed to decode the optimal bit sequence corresponding to the least TV cost function using appropriately modified node and branch metrics. Simulation results using several test images and video sequences demonstrate the improved reconstruction performance of the proposed scheme, especially in the low SNR regime (0 - 10 dB), in comparison to the conventional maximum likelihood (ML) decoder, which makes it ideally suited for implementation in practical WMSN.

This work proposes novel techniques toward the design of optimal pilot sequences to perform channel estimation in block transmission systems over wideband frequency selective wireless fading channels. The framework developed is based on minimization of the Bayesian Cramér-Rao bound (BCRB) for the mean squared error (MSE) of the channel state information (CSI) estimate. Optimal pilot signals are determined for the four predominant classes of block transmission systems, viz. single carrier zero padding (SC-ZP), multi-carrier zero padding (MC-ZP), single carrier cyclic prefix (SC-CP) and multi-carrier cyclic prefix (MC-CP) systems. This makes the techniques developed general in nature and thus applicable in a wide variety of block transmission systems. As part of this study, succinct expressions and results are also derived to characterize the error rate performance, incorporating also the effect of CSI estimation error resulting due to the proposed algorithms. Finally, numerical results obtained via Monte-Carlo simulation are presented to illustrate and compare the CSI acquisition performance of optimal pilot designs with that of conventional designs and also validate the theoretical analysis for the error rate performance.

A sparse channel state information (CSI) estimation model is proposed for reducing the pilot overhead of orthogonal time frequency space (OTFS) modulation aided multipleinput multiple-output (MIMO) systems. Explicitly, the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI that leads to a reduction of the pilot overhead, training duration and pre-processing complexity. Furthermore, it completely avoids placing multiple DD-domain guard intervals corresponding to each transmit antenna within the same OTFS frame, while keeping the training duration flexible, hence increasing the bandwidth efficiency. A unique benefit of the proposed CSI estimation model is that it can efficiently handle fractional Dopplers also. The resultant DD-domain CSI becomes simultaneously row and group (RG)-sparse. To exploit this compelling property, an orthogonal matching pursuit (OMP)-based RGOMP technique is developed, conveniently complemented by an enhanced Bayesian learning (BL)-based RG-BL framework, both of which substantially outperform the state-of-the-art methods. Furthermore, low-complexity linear detectors are designed for the ensuing data detection phase, which directly employ the estimated DD-domain sparse CSI, without assuming any further knowledge concerning the number of dominant multipath components. Finally, simulation results are provided to demonstrate performance improvement of the proposed BL-based schemes over the OMP and the state-of-the-art schemes.

Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). Our proposed transceiver designs rely on majorization theory and hyperparameter estimates obtained from the BL module for minimizing the mean square error (MSE) of parameter estimation at the fusion center (FC). The linear transceiver design framework is initially proposed for the general scenario with arbitrary SNR sensor observations, followed by a special case with high-SNR sensor observations scenario. Our analysis also incorporates the channel correlation. The MMSE channel estimates are determined for the sensors (SNs), followed by a robust transceiver design procedure that is resilient to the channel state information (CSI) uncertainty arising due to the channel estimation error, an aberration that is unavoidable in practical implementations. Our simulation results demonstrate the improved performance of the proposed BL framework and optimal MMSE transceiver design in sparse parameter estimation relying on realistic imperfect channel estimates over the benchmarks.

This paper develops a novel data dependent superimposed training technique for channel estimation in generic block transmission (BT) systems comprising of single/multi-carrier (SC/MC) and zero-padded (ZP)/ cyclic prefix (CP) systems. The training sequence comprises of the summation of a known training sequence and a data-dependent sequence that is not known to the receiver. A unique aspect of the scheme is that the channel estimation is not affected by the use of a data-dependent sequence. The pilot design framework is conceived in order to minimize the Bayesian Cramer-Rao bound (BCRB) associated with channel estimation error. Simulation results are provided to exhibit the performance of the proposed scheme for single and multi carrier zero-padded and cyclic prefixed systems.

In this paper superimposed pilot based channel estimation technique is proposed for MIMO coded FBMC systems. In coded FBMC intrinsic interference is mitigated by using spreading of symbols across time. Superimposed pilot based channel estimation is a technique that improves the spectral efficiency by transmitting the pilot symbols along with the data symbols on a set of selected subcarriers. The proposed system achieves the same bit-error performance and mean square error performance as that of MIMO-OFDM with an additional advantage of improved spectral efficiency. The spectral efficiency is improved in two ways. By removing the cyclic prefix overhead and by avoiding the use of dedicated subcarriers for pilots.

This paper considers and analyzes the performance of semiblind, training, and data-aided channel estimation schemes for multiple-input multiple-output (MIMO) filter bank multicarrier (FBMC) systems with offset quadrature amplitude modulation. A semiblind MIMO-FBMC (SB-MF) channel estimator is developed that exploits both the training symbols and second-order statistical properties of the data symbols, which leads to a significant decrease in the mean squared error (MSE) with respect to its conventional training-based counterpart. Its performance is compared with that of the interference approximation method-based least squares MIMO-FBMC (LS-MF) channel estimator, wherein the channel is estimated using exclusively training symbols. The Cramer–Rao lower bounds are derived to characterize the MSE of the proposed and LS-MF estimators, which interestingly demonstrate that while the MSE per parameter of the proposed scheme decreases with the number of receive antennas, it remains constant for the training-based scheme. The resulting bit error rates are derived for the proposed SB-MF and LS-MF channel estimators. An expectation maximization-based data-aided MIMO-FBMC channel estimator is also investigated that performs iterative maximum a posteriori channel estimation in the E-step followed by data detection in the M-step. A comparative analysis is presented for the computational complexities of the various schemes. Simulation results with practical channel models demonstrate that the proposed semiblind scheme significantly outperforms the training-based and data-aided schemes.

Motivated by the numerous healthcare applications of molecular communication inside blood vessels of the human body, this paper considers multiple relay/cooperative nanomachine (CN)-assisted molecular communication between a source nanomachine (SN) and a destination nanomachine (DN) where each nanomachine is mobile in a diffusion-advection flow channel. Using the first hitting time model, the impact of the intermediate CNs on the performance of the aforementioned system with fully absorbing receivers is comprehensively analyzed taking into account the presence of various degrading factors, such as inter-symbol interference, multi-source interference, and counting errors. For this purpose, the optimal decision rules are derived for symbol detection at each of the CNs and the DN. Furthermore, closed-form expressions are derived for the probabilities of detection and false alarm at each CN and DN, along with the overall end-to-end probability of error and channel achievable rate for communication between the SN and DN. Simulation results are presented to corroborate the theoretical results derived and also to yield insights into the system performance under various mobility conditions.

This paper develops schemes for block-sparse channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems that exploit the spatial sparsity inherent in such channels. Initially, a novel sparse Bayesian learning (SBL) based block-sparse channel estimation technique is developed for a mmWave hybrid MIMO system with multiple measurement vectors, which overcomes the shortcomings of the existing orthogonal matching pursuit-based framework. This is subsequently extended to a temporally correlated block-sparse mmWave MIMO channel. Further, an online recursive hierarchical Bayesian Kalman Filter is developed for the estimation of a time-selective mmWave MIMO channel. Bayesian Cramer–Rao bounds are also derived for the proposed static and time-selective mmWave MIMO channel estimation schemes followed by precoder/combiner design employing the SBL-based imperfect channel estimates. Simulation results are presented to demonstrate the improved performance of the proposed SBL-based channel estimation techniques in comparison to the popular OMP-based scheme proposed recently.

This study analyses the performance of a non-orthogonal multiple access-based two-way relaying system. Analytical expressions are obtained to characterise the outage probability and outage floor of the system for end-to-end communication over Rayleigh fading channels. Furthermore, results are also presented for the resulting individual user and sum ergodic capacities. A user power allocation technique is proposed for sum-rate maximisation with user quality of service constraints by transforming the original non-convex problem into a difference of convex program that can be readily solved. Finally, simulation results are presented to authenticate the analytical expressions obtained for the various quantities and illustrate the efficacy of the proposed scheme.

A novel sparse channel state information (CSI) estimation scheme is proposed for orthogonal time frequency space (OTFS) modulated systems, in which the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI. The proposed CSI estimation model leads to a reduction in the pilot overhead as well as the training duration required. Furthermore, it does not require a DD-domain guard interval between the pilot and data symbols, hence increasing the bandwidth efficiency. A novel Bayesian learning (BL) framework is proposed for CSI acquisition, which exploits the DD-domain sparsity for improving the estimation accuracy in comparison to the conventional minimum mean squared error (MMSE)-based scheme. A low complexity linear MMSE detector is used in the subsequent data detection phase. Our simulation results demonstrate the performance improvement of the proposed BL-based scheme over the conventional MMSE-based scheme as well as over other existing sparse estimation schemes.

Adaptive block-based least-mean squares (BLMS)- based techniques are conceived for channel estimation in singlecarrier (SC) wideband millimeter wave (mmWave) hybrid MIMO systems. In this context, a frequency-domain channel estimation model is developed for SC wideband systems, followed by a novel fast BLMS (FBLMS) technique, which has a significantly lower computational complexity than the existing channel estimation schemes designed for mmWave hybrid MIMO systems. The proposed FBLMS technique is also robust, since it does not require any second-order statistical information, such as the cross-covariance vector and covariance matrix. Next a beamspace domain representation of the mmWave MIMO channel is obtained, followed by the development of the sparse-FBLMS (SFBLMS) scheme for the estimation of the wideband mmWave MIMO channel that additionally exploits the angular-sparsity forimproved channel estimation performance. Analytical expressions are derived for the mean squared estimation error (MSEE) and mean squared observation error (MSOE) of both the proposed FBLMS and SFBLMS techniques. Furthermore, a systematic procedure is developed for determining the beneficial range of the values of the regularization parameter, which ensures a high channel estimation accuracy of the SFBLMS over FBLMS. A hybrid precoder and combiner design is also proposed for SCwideband systems by employing the channel estimates obtained using the above techniques. Simulation results are presented to illustrate the performance of the proposed BLMS-based schemes in comparison to the existing schemes, which closely match the theoretical results derived.

This paper designs a novel multiple measurement vector (MMV)-based sparse Bayesian learning (MSBL) technique for wideband channel estimation in single-carrier (SC) mmWave hybrid multiple input multiple output (MIMO) systems. The notable features of the proposed technique are that it leverages the simultaneous sparsity innate in the beamspace channel matrix across subcarriers, which has hitherto been unexplored by approaches that perform decoupled estimation of the individual sparse channel at each subcarrier. Furthermore, the framework developed also incorporates the effect of colored noise due to radio-frequency (RF)-combining, which has been ignored in other similar works. The realization of the frequency-domain (FD) simultaneous-sparse channel state information (CSI) acquisition model is rendered possible via the transmission of zero-padded (ZP) training blocks using a suitable choice of pilot symbols, followed by the overlap-and-sum processing module at the receiver. The Bayesian Cramer-Rao lower bound (BCRLB) is also derived to characterize the lower bound for the mean-squared-error (MSE) of channel estimation, which is achieved by a genie estimator with known channel support. Our simulation results demonstrate the enhanced performance of the proposed MMV-based MSBL approach over the conventional single measurement vector (SMV)-based SBL and the existing OMP techniques, which is also close to various benchmarks.

Hierarchical Bayesian Kalman filter (HBKF) based schemes are conceived for doubly-selective sparse channel estimation in orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless systems. Initially, a pilot based multiple measurement vector (MMV) model is formulated for estimating the OSTBC MIMO-OFDM channel. This is followed by the development of a low-complexity, online pilot-based HBKF (P-HBKF) scheme for tracking the sparse time-varying frequency-selective channel. The salient advantages of the proposed P-HBKF technique are that it requires significantly lower number of pilot subcarriers, while also exploiting the inherent sparsity of the wireless channel. Subsequently, data detection is also incorporated in the proposed framework, leading to the development of a procedure for joint sparse doubly-selective channel estimation and symbol detection. Recursive Bayesian Cramer-Rao bounds and closed form expressions are also obtained for the asymptotic mean square error (MSE) based on the solution of the Riccati equation for the KF for benchmarking the performance. Simulation results are presented for validating the theoretical bounds and for comparing the performance of the proposed and existing techniques.

This work develops a novel scheme for doubly-selective sparse channel estimation in a space-time trellis coded (STTC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless systems. Toward this end, a pilot based doubly-selective sparse channel estimation model is developed, wherein the time-evolution of the wireless channel is modeled using a first-order autoregressive (AR1) process. This is followed by the proposed online pilot-based sparse Kalman filter (P-SKF) for channel estimation. The proposed P-SKF scheme combines the advantages of the Kalman filter (KF) and sparse Bayesian learning (SBL) techniques to exploit the temporal correlation as well as the sparse multipath delay profile of the wireless channel. In addition, the proposed PSKF technique also exploits the simultaneous-sparsity across all the transmit-receive antenna pairs for improved channel estimation. The recursive Bayesian Cramer-Rao lower bound (BCRLB) is also derived to benchmark the mean square error (MSE) performance of the proposed technique. Simulation results are presented to evaluate the MSE and frame error rate (FER) performance of the proposed technique and compare with the existing schemes.

This work considers a finite blocklength (FBL) non-orthogonal multiple access (NOMA)-based joint one-way and two-way relaying aided communication scheme wherein two source nodes share a single decode-and-forward (DF) relay to exchange their information as well as transmit it to the respective destination nodes over a finite number of channel uses. Novel closed-form expressions have been obtained for the end-to-end block error rate (BLER) at all the nodes and also the net throughput of the system. Furthermore, an asymptotic end-to-end BLER floor has also been obtained for all the nodes at high transmit signal to noise power ratio (SNR). Simulation results are presented to authenticate the analytical results derived and demonstrate the efficacy of the proposed scheme.

This work proposes symbol and block level adaptive channel estimation schemes, based on the least mean squares (LMS) and block-LMS (BLMS) approaches, respectively, for multiuser-MIMO (MU-MIMO) systems. The proposed schemes do not require knowledge of the first and second order statistics of the time-varying MU-MIMO channel, while also having a lower computational complexity in comparison to the Kalman filter based channel estimation approaches present in the existing literature. Another important aspect of the proposed MU-MIMO framework is that channel estimation is carried out at the base station (BS), which simplifies the receiver architecture. Analytical expressions are derived for the error covariance matrix at each time instant and the asymptotic mean square error (MSE) of the proposed LMS and BLMS frameworks. Further, a superimposed pilot (SIP) framework for MU-MIMO channel estimation been developed, which transmits data symbols to a group of selected users during the training phase, thus leading to a significant improvement in the sum-rate performance. Simulation results are presented to demonstrate the improved sum-rate and MSE performance of the proposed schemes and also to verify the analytical results.

This work considers the design of linear minimum mean square error (MMSE) precoders and combiners for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) wireless sensor network. The proposed designs that minimize the mean squared error (MSE) of the parameter estimate at the fusion center are based on majorization theory, which leads to non-iterative closed-form solutions for the precoders and combiners. Various scenarios are considered for parameter estimation such as networks with ideal high precision sensors as well as noisy non-ideal sensors. Moreover, inter parameter correlation is also incorporated, which makes the analysis comprehensive. The Bayesian Cramer-Rao bound (BCRB) and centralized MMSE bound are determined to characterize the estimation performance. Simulation results demonstrate the improved performance and also corroborate our analytical formulations. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This work analyzes the performance of an underlay cognitive radio based decode-and-forward mixed multiple-input multiple-output (MIMO) radio frequency/free space optical (RF/FSO) cooperative relay system with multiple mobile secondary and primary user nodes. The effect of imperfect channel state information (CSI) arising due to channel estimation error is also considered at the secondary user transmitters (SU-TXs) and relay on the power control and symbol detection processes respectively. A unique aspect of this work is that both fixed and proportional interference power constraints are employed to limit the interference at the primary user receivers (PU-RXs). Analytical results are derived to characterize the exact and asymptotic outage and bit error probabilities of the above system under practical conditions of node mobility and imperfect CSI, together with impairments of the optical channel, such as path loss, atmospheric turbulence, and pointing errors, for orthogonal space-time block coded transmission between each SU-TX and relay. Finally, simulation results are presented to yield various interesting insights into the system performance such as the benefits of a midamble versus preamble for channel estimation., Comment: revision submitted to IEEE Transactions on Cognitive Communications and Networking

This paper presents sparse Bayesian learning (SBL)-based schemes for approximately sparse channel estimation in an orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless system. The parameterized prior-based SBL framework is employed to present a pilot scheme for an ill-posed OSTBC MIMO-OFDM channel estimation scenario. Maximum likelihood symbol detection (MLSD) has been incorporated in the expectation-maximization framework for SBL-based channel estimation. This has led to the development of a novel scheme for joint approximately sparse channel estimation and symbol detection. The proposed scheme performs SBL-based channel estimation in the E-step followed by a modified ML decision metric-based symbol detection in the M-step. Bayesian Cramer–Rao bounds are obtained for the genie minimum mean-squared error estimators corresponding to the SBL schemes. Closed-form bit error probability expressions are derived for the MLSD in the presence of SBL-based channel estimation errors. Simulation results are presented towards the end to validate the theoretical bounds and illustrate the performance of the proposed techniques.

This paper proposes optimal superimposed pilot (SIP) sequence design and pilot placement towards mean square error (MSE) minimization for channel estimation in spatially correlated/uncorrelated MIMO–OFDM channels. For uncorrelated channels, a closed form expression is derived for the optimal pilot sequence, number of pilot subcarriers and pilot locations. For correlated channels, the problem of designing the optimal SIP sequence is formulated as a semidefinite program. The proposed schemes lead to a substantial increase in the bandwidth efficiency since it employs pilot symbols on a significantly fewer number of subcarriers in comparison to existing schemes. It is shown that the proposed techniques achieve the Cramer–Rao bounds. In addition, a closed form analytical expression is derived for the bit-error rate of orthogonal space–time block codes (OSTBC) based MIMO–OFDM systems including the effect of channel estimation error arising from the proposed SIP based estimation techniques. Simulation results demonstrate the improved estimation performance and bandwidth efficiency of the proposed schemes.

This paper considers the problem of cooperative spectrum sensing in multiuser multiple-input multiple-output cognitive radio networks considering the presence of uncertainty in the channel state information (CSI) of the secondary user channels available at the fusion center. Several schemes are proposed that employ cooperative decision rules based on local sensor decisions transmitted to the fusion center by the cooperating nodes over an orthogonal multiple access channel. First, fusion rules are derived under perfect CSI at the fusion center for both antipodal and nonantipodal signaling. Then, a robust detector, termed the uncertainty statistics-based likelihood ratio test, which optimally combines the decisions of different secondary users, is obtained for scenarios with CSI uncertainty. A generalized likelihood ratio test based robust detector is also derived for this scenario. Closed-form expressions are obtained to characterize the probabilities of false alarm $(P_{\text{FA}})$ and detection $(P_D)$ at the fusion center. Simulation results are presented to compare the performance of the proposed schemes with that of the conventional uncertainty agnostic detectors and also to corroborate the analytical expressions developed.

The asymptotic high-signal-to-noise ratio (SNR) symbol error rate (SER) performance of selective decode-and-forward cooperative multiple relay-aided wireless systems is derived for $M$ -PSK and $M$ -QAM modulations. The proposed analysis considers both dual-phase and multi-phase relaying protocols. Furthermore, analytical results are also presented for optimal power allocation both at the source and at each of the relays, since it significantly influences the performance of cooperative communication. A novel aspect of the proposed framework is that the SER and optimal power allocation results derived are applicable to diverse fading channels such as $\eta -\mu $ , $\kappa -\mu $ , and shadowed-Rician scenarios and each for non-identical fading for the source–destination, source–relay (SR), and relay–destination (RD) links. The applicability of the proposed framework is also demonstrated for diverse PHY layer schemes, such as multiple-input multiple-output-orthogonal space–time block codes, cooperative beamforming, joint transmit/receive antenna selection, and free-space optical scenarios. This high-SNR analysis provides the valuable insights into the impact of the diversity orders of the SR and RD links on the end-to-end SER as well as on the optimal power allocation factors both in the dual-phase and multi-phase protocols of various fading channels and schemes. Our simulation results verify the analytical results derived.

This work conceives techniques for the design of linear hybrid precoders toward decentralized parameter estimation in a millimeter wave (mmWave) wireless sensor network (WSN). To achieve this objective, a novel system model is proposed for mmWave WSNs, wherein the sensors pre-process their observations using hybrid baseband and radio frequency (RF) precoders, prior to their transmission over a coherent multiple access channel (MAC) to the fusion center (FC). The pertinent combiner is constrained to satisfy the constant signal gain condition, thus ensuring a distortionless estimate at the FC without requiring any additional post-processing. The precoders are designed to achieve the minimum mean square error (MSE) of parameter estimation at the FC. Furthermore, robust precoder designs are also developed to mitigate the effects of imperfect channel state information (CSI) in a mmWave sensor network, which is inevitable due to practical effects such as limited pilot overhead, channel drift, quantization error, among others. Robust designs are presented considering both the probabilistic as well as bounded CSI uncertainty models, which makes the design framework comprehensive since it considers both average and worst case distortion minimization. The centralized MMSE lower bound is also derived to characterize the estimation performance of the proposed schemes. Simulation results are presented to demonstrate the performance of the proposed designs and also verify the analytical propositions.

This letter proposes novel precoding techniques for robust decentralized parameter estimation with only imperfect channel state information (CSI) in a coherent multiple access channel based wireless sensor network. For scalar parameter estimation, the proposed technique minimizes the worst case estimation error arising due to the channel uncertainties while ensuring the maximum gain at each receive antenna. For vector parameter estimation, since the general noisy measurement case is intractable, a robust precoder is developed for noiseless sensor measurements that eliminates the need for postprocessing at the fusion center while simultaneously minimizing the worst case estimation error. Simulation results show that the proposed techniques have a superior performance in comparison to imperfect CSI agnostic estimation techniques.

This paper proposes a novel affine superimposed pilot design scheme for channel estimation in general block transmission schemes that is applicable for both single/multiple carrier (SC/MC) and zero-padded(ZP) as well as cyclic prefixed (CP) systems. The proposed approach ensures orthogonality between the pilot and data symbols to mitigate the mutual interference between training and data in superimposed pilot design. The pilot design is based on minimization of the Bayesian Cram\'er-Rao bound (BCRB) for mean square error (MSE) of the channel estimation error subject to total pilot power constraint. The proposed technique also incorporates the channel correlation matrix for improved estimation. The optimal pilot placement is formulated as an A- Optimal design-based semi-definite program (SDP) that can be solved efficiently. Simulation results are presented for single carrier and multi carrier zero padded and cyclic prefixed systems to demonstrate the performance of the proposed scheme.

This work develops a sparse Bayesian learning based Kalman filtering (SBL-KF) framework for channel estimation in frequency and time selective, i.e. doubly selective, millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Towards this end, the doubly selective mmWave MIMO channel is represented in terms of the beamspace channel vector employing suitable sparsifying dictionary matrices comprising of the transmit and receive array response vectors. Subsequently, a multiple measurement vector (MMV) model is developed for mmWave MIMO channel estimation that also leverages the group-sparsity inherent in the equivalent beamspace channel, thus leading to improved performance. The Bayesian Cramer-Rao bound (BCRB) is also derived to benchmark the estimation performance. Simulation results are presented to demonstrate the improved performance of the proposed scheme in comparison to the existing schemes based on OMP and also the performance improvement from leveraging group-sparsity.

This paper investigates the uplink asymptotic performance of single-cell multiuser multiple-input multiple-output(MU-MIMO) system with a very large antenna array for time-selective fading channels, resulting from user mobility. To exploit the temporal correlation of the channel, the Kalman filter (KF) is developed for channel estimation, followed by its asymptotic performance analysis. A lower bound on the uplink achievable rate is obtained in the asymptotic limit of a large number of time slots and a finite number of base station (BS) antennas for linear receivers, such as the maximum ratio combiner (MRC) and zero-forcing (ZF) receiver. In addition the pertinent power scaling law is also derived for a large number of antennas. Finally, simulation results are presented to validate the analytical results.

A novel total variation (TV) framework is conceived for joint detection and dynamic state estimation (JDSE) for wireless transmission from the measurement devices to the control center in a smart grid. The proposed scheme employs a TV regularization based decoder in conjunction with a Kalman filter-based dynamic power system state estimator to minimize the detection error for transmission of the measurements over a fading wireless channel. A novel application of the Viterbi algorithm is proposed for TV detection of the received measurement vectors. Furthermore, the proposed JDSE scheme is also extended to a system in which each measurement is quantized to a single bit. This reduced-bandwidth-based TV-JDSE leads to a significant bandwidth efficiency improvement in the smart grid and to improved state estimation. Our simulation results provided for the standard IEEE-14 bus test system under different operating conditions demonstrate improved performance in comparison to conventional techniques and they are capable of approaching the ideal Clairvoyant Kalman filter benchmark.

This work develops a novel sparse Bayesian learning (SBL)-based channel estimation technique for frequency-selective millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Towards this end, the concatenated frequency-selective MIMO channel matrix is represented in terms of the beamspace channel vector employing suitable transmit and receive array response dictionary matrices. Subsequently, a multiple measurement vector (MMV) model is developed for estimation of the sparse beamspace channel vector considering the block transmission of zero-padded training frames. The unique aspects of the proposed scheme are that it exploits the group-sparsity inherent in the equivalent beamspace channel vector of the frequency-selective mmWave MIMO channel and also considers the effect of correlated noise in the equivalent system model due to RF-combining. This feature, coupled with the improved ability of SBL for sparse signal recovery, leads to a significantly enhanced performance of the proposed scheme in comparison to the orthogonal matching pursuit (OMP) technique proposed recently. Bayesian Cramer-Rao bounds (BCRBs) are also derived to characterize the estimation performance. Simulation results are presented to demonstrate the improved performance of the proposed SBL-based channel estimation technique in comparison to the existing scheme and also a performance close to the various benchmarks.

Motivated by the numerous healthcare applications of molecular communication (MC) inside blood vessels, this work considers relay/cooperative nanomachine (CN)-assisted mobile MC between a source nanomachine (SN) and a destination nanomachine (DN) where each nanomachine is mobile in a flow-induced diffusive channel. Using the first hitting time model, the impact of an intermediate CN on the performance of the CN-assisted diffusive mobile MC system with fully absorbing receivers is analyzed in the presence of inter-symbol interference, multi-source interference, and counting errors. For this purpose, the likelihood ratio test based optimal symbol detection scheme is obtained at the DN considering the non-ideal nature of CN, i.e., CN can be in error with a finite probability. Further, to characterize the system performance, closed-form expressions for the end-to-end probabilities of detection and false alarm at the DN are derived between the SN-DN pair incorporating the detection performance of the intermediate CN. In addition, the channel capacity expression is also derived for the aforementioned scenario. Simulation results are presented to corroborate the theoretical results derived and also, to yield insights into system performance., Submitted to 2018 Asilomar Conference

This work investigates the performance of hybrid satellite-terrestrial systems over time-selective fading links arising due to the node mobility with multiple relay based selective decode-and-forward cooperation. The aerial satellite-to-relay and satellite-to-destination links are non-identical time-selective shadowed Rician fading in nature, whose parameters depend on the elevation angle of the satellite, whereas the terrestrial relay-destination links are assumed to be non-identical time-selective generalized Nakagami faded. Closed form expressions are derived for the per-frame average symbol error rate (SER) and asymptotic SER floor considering the transmission of Mary PSK modulated symbols. It is observed that the time-varying nature of the links significantly degrades the system performance. Further, the impact of the satellite elevation angles at the terrestrial nodes is explicitly demonstrated through simulations, along with the effect of preamble versus midamble for channel estimation. The error rate of the system is seen to reduce significantly with increasing satellite elevation angle at the relay when the satellite-destination link experiences frequent heavy shadowing (FHS) and the relay-destination links are relatively strong. However, for other scenarios when the relay-destination links are relatively weak and satellite-relay links experience FHS, significant performance improvement can be seen by increasing the satellite elevation angle at the destination UE.

An Extrinsic Information Transfer (EXIT) framework to derive the BER of turbo decoding for fading MIMO wireless channels is presented in this work. Closed form BER expressions are derived for turbo decoding in maximum ratio combining (MRC), MIMO Zero Forcing, MIMO V-BLAST and MIMO beamforming wireless systems considering BPSK modulation. Simulation results demonstrate accurate BER prediction for an arbitrary number of antennas and iterations in all the turbo BER regions.

This paper develops a framework for semi-blind channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems that use orthogonal space-time block codes (OSTBCs). The proposed technique is based on a whitening unitary (WU) decomposition together with superimposed training (ST). The ST incorporates an orthogonal affine precoder to avoid interference between the pilot and data symbols during both channel estimation and data detection. The complex constrained Cramer-Rao bound (CC-CRB) is derived to characterize the resulting mean squared error (MSE) of the proposed semi-blind channel estimation scheme. Simulation results using practical IMT-2000 channel models demonstrate the improved performance of the proposed semi-blind scheme in comparison to both non-semiblind ST and conventional training techniques, in terms of the MSE and bit error rate (BER).

This paper proposes a novel technique for secondary user beamforming in cognitive radio networks (CRN). A practical scheme based on limited rate feedback from secondary users to secondary base station (SBS) and feedback from primary users to SBS are employed to acquire the secondary user channel and interference channel state information. Subsequently, a scheme is presented for regularized beamforming in a secondary user system to avoid performance degradation caused by noise amplification. The proposed scheme decomposes the optimal beamformer computation paradigm into sub-problems followed by solving them in an iterative manner. Simulation results demonstrate the better performance of the proposed practical scheme in comparison to ideal scheme.

This paper develops a novel sparse Bayesian learning (SBL)-based multiple-input multiple-output (MIMO) channel estimation technique for hybrid millimeter wave (mmWave) wireless systems by exploiting spatial sparsity in the wireless channels arising from the highly directional nature of propagation. The spatially sparse MIMO channel is modeled in terms of the basis array response matrices corresponding to the quantized directional cosines at the transmit and receive antenna arrays followed by the development of an expectation maximization (EM)-based sparse Bayesian learning (SBL) channel estimation approach. Subsequently, an enhanced variant of the SBL scheme is proposed based on hard thresholding the associated hyperparameter estimates, which is observed to significantly improve the accuracy of channel estimation. The Bayesian Cramer-Rao bound (BCRB) is also derived to benchmark the accuracy of the proposed SBL-based channel estimation schemes. Finally, simulation results are presented to illustrate the performance improvement achieved in comparison to the existing state-of-the-art orthogonal matching pursuit (OMP)-based sparse mmWave channel estimation scheme.

This paper proposes a robust algorithm for wireless transmission of phasor measurement unit (PMU) measurements in smart grids. The proposed algorithm exploits bounded variation (BV) property of the PMU measurements in a power grid towards minimizing the decoding error of the PMU measurements transmitted over the wireless channel, thus enhancing the reliability of communication. Further, a novel trellis based Viterbi decoder is developed with modified branch and state metrics for optimal low-complexity TV regularization based maximum likelihood (ML) decoding of the PMU measurements. Simulation results performed on an IEEE-14 bus power system employing practical models derived from a real national grid reveal the improved performance of the proposed total variation measurement decoding (TVMD) algorithm in comparison to the conventional ML decoding method and thus demonstrate the suitability of the proposed scheme for enhancing reliability of wireless communication in smart grids.

This paper proposes a novel approach for optimal pilot sequence design for channel estimation in single carrier (SC) and multi carrier (MC) block transmission systems over frequency selective channels. The training design is based on minimizing the Bayesian Cramer-Rao bound (BCRB) for mean square error (MSE) of channel estimation which also employs the prior knowledge of the channel in conjunction with the receiver noise covariance subject to a total transmit power constraint. The proposed scheme derives the optimal pilot sequence which is applicable to all four block transmission systems i.e. single carrier zero padding (SC-ZP), multi-carrier zero padding (MC-ZP), single carrier cyclic prefix (SC-CP) and multi-carrier cyclic prefix (MC-CP) systems. Thus, the proposed framework and results derived are general with wide applicability. Simulation results are presented to show the improved performance of the proposed pilot design scheme in comparison to a conventional pilot design, in terms of both the mean square error (MSE) and bit error rate (BER).

In this work, we present a novel random parameter-based expectation-maximization (EM) algorithm for joint symbol detection and channel estimation in fast fading space-time trellis coded (STTC) multiple-input multiple-output (MIMO) wireless communication systems. Employing the EM framework with the MIMO channel as the random parameter, we demonstrate that this paradigm reduces to the optimal Kalman filter (KF)-based channel update in the E-step followed by a modified path metric-based maximum likelihood sequence decoder (MLSD) in the M-step. Further, we also present the pairwise error probability (PEP) upper bound for the frame error rate (FER) and the Bayesian Cramer-Rao bound (BCRB) for the proposed joint estimation scheme. Simulation results are presented to demonstrate the performance of the proposed technique and validate the analytical bounds.

In this work, we develop a framework for optimal joint Source-Channel Maximum Likelihood (SCML) decoding in Wireless Sensor Networks (WSNs). The proposed scheme employs a novel Generalized Likelihood Ratio Test based Prediction Likelihood Tree (PLT) approach to exploit the spatio-temporal narrowband properties of the sensor data for sequence detection in wireless sensor networks. Further, analytical bounds are derived to characterize the performance of the low complexity decision feedback and optimal Viterbi based Maximum Likelihood Sequence Detection (MLSD) for joint decoding over fading wireless channels, where only ad hoc schemes exist in current literature. The PLT based SCML scheme, which has a low complexity, is ideally suited for implementation in practical wireless sensor networks with limited computational power and achieves a performance close to the optimal MLSD bound. Simulation results are presented to validate the performance of the SCML algorithm and the proposed analytical bounds for sensor data reception in WSNs.

This paper considers a robust precoder design for MIMO-OFDM systems with insufficient cyclic prefix (CP). Interference alignment is utilized to formulate the precoder design problem as a bit error rate (BER) minimization problem subject to a total power constraint. To this end, the precoder is designed to be robust to the errors in the available channel estimates, by considering the worst-case BER for optimization. Interestingly, channel estimates at both transmitter and receiver are allowed to be in error. Extensive simulations are carried out to compare the performance of various MIMO-OFDM systems with and without robust precoders.

This paper presents sparse Bayesian learning (SBL)-based estimation schemes for an approximately sparse wireless multipath channel impulse response (MCIR) in a space-time trellis coded (STTC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. The proposed schemes consider space-time trellis encoding over consecutive OFDM symbols and employ the multiple response extension of SBL (MSBL) framework to design a pilot-based channel estimation scheme. Subsequently, the trellis-based Viterbi decoder is systematically incorporated into the expectation maximization (EM) framework to propose a novel joint channel estimation and maximum likelihood sequence detection (MLSD) paradigm, the solution of which is shown to lead to an MSBL-based MIMO channel estimate in the E- step followed by a novel modified path-metric- based Viterbi decoder in the M-step with a reduced decoding complexity. Simulation results are presented to illustrate the superior performance of the proposed MSBL-based schemes over some of the existing non-sparse and sparse channel estimation techniques.

In this letter, we present novel detection schemes for non-antipodal signaling based cooperative spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio (CR) networks, which are robust against the uncertainty in channel estimates. We consider a scenario in which the secondary users (SU) cooperate by reporting the sensed data to the fusion center for soft combining towards primary user (PU) detection. We formulate this problem employing the optimal linear discriminant and model the uncertainties in the channel state information (CSI) as ellipsoidal uncertainty sets. It is then demonstrated that this problem of PU detection with uncertainty in the channel estimates for cooperative spectrum sensing in a CR system can be formulated as second order cone program (SOCP). Further, we extend this paradigm to the associated relaxed robust detector (RRD) and multicriterion robust detector (MRD) that maximally separate the hypothesis ellipsoids in low signal-to-noise power (SNR) and deep fade channel conditions. We present a closed form solution for the proposed robust detector for the above MIMO cooperative spectrum sensing scenario. Simulation results demonstrate a significant improvement in the detection performance of the proposed uncertainty aware robust detection schemes in comparison to the conventional uncertainty agnostic matched filter detector for cooperative MIMO PU detection.

This paper considers an Orthogonal Frequency Division Multiplexing (OFDM) based selective decode-and-forward (DF) cooperative communication system with multiple mobile relays between the mobile source and destination. Closed form expressions have been derived for the average end-to-end SER and also the asymptotic error floor considering a Rayleigh faded correlated multi tap channel with time selectivity due to node mobility and imperfect channel estimates for the source-destination, source-relay and relay-destination links. Further, a framework is also developed for optimal power allocation for a given total system power budget. Simulation results have been presented to illustrate the system performance for various OFDM channel and mobility conditions and also to verify the analytical results derived. Furthermore, it is demonstrated that optimal power allocation leads to a performance improvement in comparison to the conventional equal power allocation especially in the low and moderate SNR regimes. At high SNR, the system performance with both optimal and equal power allocation can be seen to approach an asymptotic floor, since the system performance is independent of the power of the noise components, which is also supported by the analysis.