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A new affine-precoded superimposed pilot (AP-SIP) scheme is conceived for both wireless channel and radar target parameter estimation in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems. The AP-SIP scheme leads to enhanced estimation accuracy and improved utilization of spectral resources. Initially, the pilot-assisted radar (PAR) and data-assisted radar (DAR) parameter estimation models are separately developed for the estimation of the radar target parameters. Subsequently, these are combined into a joint pilot-data radar (JPDR) model for simultaneously harnessing both the signals to further boost the estimation accuracy. The sparse Bayesian learning (BL)-based joint-BL (J-BL) technique is developed for this system that efficiently exploits the sparsity of the radar scattering environment. Next, a group sparse BL (G-BL) technique is also derived that exploits the group sparsity across subcarriers for the estimation of the wireless beamspace channel vector, which outperforms the competing techniques, including conventional sparse BL. The optimal pilot, transmit precoder (TPC) and receive combiner (RC) are determined at the dual-function radar-communication (DFRC) base station (BS) and also at the user equipment (UE) for maximizing the performance attained. The Bayesian Cramer-Rao bounds (BCRB) are explicitly derived to benchmark the performance of the wireless channel and radar target parameter estimation. Simulation results are provided to demonstrate the improved performance of the proposed schemes considering multiple metrics, such as the normalized mean squared error (NMSE), bit error rate (BER) and achievable spectral efficiency (ASE).

Asynchronous distributed hybrid beamformers (ADBF) are conceived for minimizing the total transmit power subject to signal-to-interference-plus-noise ratio (SINR) constraints at the users. Our design requires only limited information exchange between the base stations (BSs) of the mmWave multi-cell coordinated (MCC) networks considered. To begin with, a semidefinite relaxation (SDR)-based fully-digital (FD) beamformer is designed for a centralized MCC system. Subsequently, a Bayesian learning (BL) technique is harnessed for decomposing the FD beamformer into its analog and baseband components and construct a hybrid transmit precoder (TPC). However, the centralized TPC design requires global channel state information (CSI), hence it results in a high signaling overhead. An alternating direction based method of multipliers (ADMM) technique is developed for a synchronous distributed beamformer (SDBF) design, which relies only on limited information exchange among the BSs, thus reducing the signaling overheads required by the centralized TPC design procedure. However, the SDBF design is challenging, since it requires the updates from the BSs to be strictly synchronized. As a remedy, an ADBF framework is developed that mitigates the inter-cell interference (ICI) and also control the asynchrony in the system. Furthermore, the above ADBF framework is also extended to the robust ADBF (R-ADBF) algorithm that incorporates the CSI uncertainty into the design procedure for minimizing the the worst-case transmit power. Our simulation results illustrate both the enhanced performance and the improved convergence properties of the ADMM-based ADBF and R-ADBF schemes.

Low-complexity fusion rules relying on hybrid combining are proposed for decision fusion in frequency selective millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) sensor networks (SNs). Both centralized (C-MIMO) and distributed (D-MIMO) antenna architectures are considered, where the error-prone local sensor decisions are transmitted over orthogonal subcarriers to a fusion center (FC) employing a large antenna array. Fusion rules are designed for the FC, followed by closed-form expressions of the false alarm and detection probabilities to comprehensively characterize the performance of distributed detection. Furthermore, efficient transmit signaling vectors are designed for optimizing the detection performance. Both the asymptotic performance analysis and the pertinent power reduction laws are presented for the large antenna regime considering both the C-MIMO and D-MIMO topologies, which potentially lead to a significant transmit power reduction. Low-complexity fusion rules and their analyses are also given for the realistic scenario of incorporating channel state information (CSI) uncertainty, where the sparse Bayesian learning (SBL) framework is utilized for the estimation of the sparse frequency selective mmWave massive MIMO channel. Finally, the performance of the proposed low-complexity detectors is characterized through extensive simulation results for different scenarios.

The downlink (DL) of a reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) multiple-input multiple-output (MIMO) system relying on a non-diagonal RIS (NDRIS) phase shift matrix is considered. A max-min fairness (MMF) problem is formulated under the total transmit power constraint while employing joint active hybrid beamforming (HBF) both at the base station (BS) as well as at each user equipment (UE), and passive beamforming at the NDRIS. To solve this non-convex problem, a sequential optimization method is conceived, wherein the UE having the poorest channel is identified first, which is termed as the worst-case UE. Then the phase shifter coefficients of the NDRIS are optimized using the alternating direction method of multipliers (ADMM) followed by the hybrid transmit precoder (TPC) and receiver combiner (RC) design using the Karcher mean, the least squares and the regularized zero forcing (RZF) principles. Finally, the optimal power allocation is computed using the path-following algorithm. Simulation results show that the proposed NDRIS-HBF system yields an improved worst-case UE rate in comparison to its conventional diagonal RIS (DRIS)-HBF counterpart, while approaching the half-duplex relay (HDR)-HBF benchmark for large values of the number of reflecting elements (REs). Furthermore, the energy efficiency (EE) of the NDRIS structure is significantly higher than that of the DRIS, HDR systems, while being higher than that achieved by the full-duplex relay (FDR) system at high SNR.

Sparse multipath channel impulse response (CIR) estimation schemes are conceived for optical orthogonal frequency division multiplexing (O-OFDM) visible light communication (VLC) systems. We commence by deriving the input-output models for both asymmetrically clipped optical OFDM (ACO-OFDM) and direct current-biased optical OFDM (DCO-OFDM) systems. A multipath CIR model is derived that captures both the diffusive as well as specular reflections of the VLC channel. Next, we introduce both the sparsity-agnostic conventional least square (LS) and the linear minimum mean square error (LMMSE) channel estimation (CE) techniques. This is followed by the orthogonal matching pursuit (OMP)-based sparse recovery technique, which exploits the delay-domain sparsity of the CIR. Furthermore, a novel sparse multipath CIR estimation scheme is proposed using the Bayesian learning (BL) framework, which requires only a limited number of pilot subcarriers, hence resulting in a reduced pilot overhead as compared to other state-of-the-art (SoA) CE techniques. The Bayesian Cramer Rao lower bound (BCRLB) as well as the Oracle-minimum mean squared error (O-MMSE) estimator are also derived for benchmarking the estimation performance of the proposed BL-based framework. Our simulation results demonstrate that the proposed BL method outperforms other existing sparse and conventional CE methods in terms of various metrics, such as the normalized mean-square-error (NMSE), the outage probability (OP), and the bit error-rate (BER) despite its reduced pilot overhead.

In this work, frequency selective hybrid precoders and combiners are designed for a millimeter wave (mmWave) multiuser (MU) multiple-input-multiple-output (MIMO) downlink underlay cognitive radio network (CRN) utilizing multiple radio frequency (RF) chains and uniform rectangular planar arrays (URPAs) both at the CR base station (CBS) and the secondary users (SUs). The proposed designs maximize the downlink spectral efficiency (SE) of the secondary users, while keeping the interference introduced to the primary user within a prescribed threshold. In the first phase, considering only the channel knowledge at each subcarrier, a novel blind hybrid MMSE-RC (minimum mean squared error-receiver combiner) design is determined using the modified simultaneous orthogonal matching pursuit (MSOMP). Further, employing feedback for each SU’s effective channel, a two-stage decoupled strategy is developed for hybrid transmit precoder (TPC) design, wherein the RF and stage-1 BB-TPC are designed in the first step relying on a capacity-optimal fully digital (FD)-TPC approximation problem followed by the stage-2 BB-TPC design using a low-complexity ZF approach with the goal of mitigating the MUI. Towards this, a novel Modified Alternating Minimization-Zeroforcing (MAM-ZF) algorithm is proposed to compute the hybrid-TPC weights. Furthermore, a low complexity alternating minimization-zeroforcing (LAM-ZF)-based precoding algorithm is also proposed towards the same. Finally, a per-subcarrier optimal power loading solution is derived in closed-form with the objective of maximizing the sum SE while satisfying the interference and transmit power budget limitations. Our simulation findings show that the proposed schemes outperform other state-of-the-art techniques and achieve a spectral efficiency comparable to that achieved by fully-digital beamforming, while requiring a significantly fewer number of RF chains.

The energy efficiency (EE) of the reconfigurable intelligent surface (RIS) aided multiuser (MU) millimeterwave (mmWave) multi-input multi-output (MIMO) downlink is maximized by jointly optimizing the transmit power and number of active radio frequency (RF) chains. The base band (BB) transmit precoder (TPC) of this system is derived first by using the zero-forcing (ZF) technique for a given RF TPC and combiner at the base station (BS) as well as the phase shift matrix at the RIS. Furthermore, the ergodic sum-rate of the system is derived assuming a large number of antennas at the BS and users, followed by formulating the EE maximization problem subject to specific constraints imposed on the transmit power and on the number of RF chains. Then a low complexity sequential method is proposed for solving the above optimization problem, thus determining both the optimal transmit power and the number of active RF chains. Finally, simulation results are presented for demonstrating the improved EE performance of an RIS-aided mmWave MIMO system for a limited number of RF chains and transmit power at the BS.

A hybrid transceiver architecture is conceived for a cognitive radio (CR) aided millimeter wave (mmWave) multiuser (MU) multiple-input multiple-output (MIMO) downlink system relying on multiple radio frequency (RF) chains both at the CR base station (CBS) and the secondary users (SUs). To begin with, a hybrid transceiver design algorithm is proposed for the CBS and SUs, to maximize the sum spectral efficiency (SE) by decoupling the hybrid transceiver into a blind minimum mean squared error (MMSE) receiver combiner (RC) and optimal-capacity two-stage hybrid transmit precoder (TPC) components. These RC-weights and TPC-weights are subsequently found by using the popular simultaneous orthogonal matching pursuit (SOMP) technique. A closed-form solution is derived for the optimal power allocation that maximizes the sum SE under the associated interference and transmit power constraints. To achieve user fairness, we also propose an optimal power allocation scheme for maximizing the geometric mean (GM) of the SU rates. Finally, a low-complexity limited feedback aided hybrid transceiver is designed, which relies on the random vector quantization (RVQ) technique. Our simulation results demonstrate that an improved SE is achieved in comparison to the state-of-the-art techniques.

This work conceives novel target detection and parameter estimation schemes in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) radar (mMR) systems for both stationary and mobile targets/radar platform. Initially, the orthogonal matching pursuit (OMP)-based mmR (OmMR) algorithm is proposed for stationary targets to estimate their radar cross-section (RCS) coefficients, angle, range locations together with the number of targets. Next, mMR systems with mobile targets and platform are considered, followed by development of the simultaneous OMP (SOMP)-based mMR (SmMR) algorithm for RCS, angle/range estimation together with their Doppler velocities. The proposed algorithms lead to a significant improvement in performance since they exploit the inherent sparsity of the mMR scattering scene in contrast to the conventional schemes. Two-dimensional (2D) mMR imaging procedures are also presented for both scenarios in the angle, range, and Doppler dimensions. Analytical expressions are derived for the Cramér-Rao bounds (CRBs) for the mean-squared error (MSE) of joint estimation of the RCS coefficients and Doppler velocities. Simulation results demonstrate that proposed schemes perform well even in low signal-to-noise ratio (SNR) scenarios with a few snapshots of the scattering environment and yield improved performance in comparison to existing sparse as well as non-sparse schemes.

This work conceives dictionary-learning (DL)-based sparse channel estimation schemes for multi-user Terahertz (THz) hybrid MIMO systems incorporating also non-idealities such as hardware impairments and beam-squint effect. Due to the presence of large antenna arrays coupled with frequency selectivity, beam squint effect is significant in THz systems. Moreover, the manufacturing and calibration errors that inevitably arise during the production of antenna arrays result in hardware impairments such as irregular antenna spacing, mutual coupling and antenna gain/phase errors in practical THz systems. To overcome these problems, this work proposes a DL algorithm to determine the best sparsifying dictionary from the acquired observations for a single-carrier frequency domain equalization (SC-FDE)-based wideband THz system in the presence of hardware impairments as well as the beam squint effect. The dictionary thus obtained is subsequently employed to exploit the sparsity of the MIMO THz channel toward CSI estimation. Furthermore, the Cramér-Rao lower bound (CRLB) is also derived for the joint DL and CSI estimation algorithm, which acts as a benchmark for the mean-squared error (MSE) performance of the channel estimate obtained. The scheme is also extended to SC-FDE-based wideband THz MIMO systems with multiple antenna users. Simulation results are presented to corroborate our analytical findings and also demonstrate the improved performance with respect to the agnostic scheme that ignores the non-idealities.

This work conceives techniques for the design of hybrid precoders/combiners for optimal bit allocation in frequency selective millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, toward transmission rate maximization. Initially, the optimal fully digital ideal precoder/ combiner design is derived together with a closed-form expression for the optimal bit allocation in the above system. This is followed by the development of a framework for optimal transceiver design and bit allocation in a practical mmWave MIMO-OFDM implementation with a hybrid architecture. It is demonstrated that the pertinent problem can be formulated as a multiple measurement vector (MMV)-based sparse signal recovery problem for joint design of the RF and baseband components across all the subcarriers, and an explicit algorithm is derived to solve this using the simultaneous orthogonal matching pursuit (SOMP). To overcome the shortcomings of the SOMP-based greedy approach, an MMV sparse Bayesian learning (MSBL)-based state-of-the-art algorithm is subsequently developed, which is seen to lead to improved performance due to the superior sparse recovery properties of the Bayesian learning framework. Simulation results verify the efficacy of the proposed designs and also demonstrate that the performance of the hybrid transceiver is close to that of its fully-digital counterpart.

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.

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 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, κ - μ, η - μ, 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.

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 paper presents novel sparse Bayesian learning (SBL)-based target imaging and parameter estimation techniques in monostatic multiple-input multiple-output (MIMO) radar systems for practical scenarios with insufficient observation samples and unknown target parameters. First, the SBL framework is developed for a single measurement vector setting with an underlying sparse target reflectivity parameter vector. This is subsequently extended to scenarios with multiple observation snapshots considering uncorrelated as well as correlated target reflectivity parameters. Variants are also proposed for challenging scenarios considering the presence of ground clutter. Cramér-Rao bounds are derived for the reflectivity, Doppler, and range estimates to comprehensively characterize the performance of the proposed estimation schemes. A joint parameter estimation and imaging scheme is developed based on a Taylor series expansion of the MIMO radar dictionary matrix. Simulation results demonstrate enhanced imaging and estimation accuracy of the proposed SBL schemes in comparison with the existing techniques for MIMO radar systems.

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.

We present novel schemes for optimal OFDMA bitrate allocation towards video quality maximization in H.264 scalable video coding (SVC)-based 4G wireless systems. We use the rate and quality models for video characterization of the SVC extension of the H.264/AVC and develop the framework for optimal scalable video transmission. Subsequently, we derive the closed form solution of the optimal H.264 scalable video quantization parameter for sum video quality maximization in unicast and multicast 4G WiMAX adaptive modulation and coding (AMC) scenarios. We also formulate a Vickrey-Clarke-Groves (VCG) auction-based time-frequency (TF) resource pricing scheme for dynamic bitrate allocation and simultaneous prevention of video quality degradation by malicious users for H.264-based scalable video transmission. Simulation results demonstrate that application of the proposed optimal 4G OFDMA schemes for unicast/multicast video quality maximization yield significantly superior performance in comparison to fixed rate video agnostic allocation.

We consider the problem of optimal H.264 scalable video scheduling, with an objective of maximizing the end-user video quality while ensuring fairness in 3G/4G broadband wireless networks and video sensor networks. We propose a novel framework to characterize the video quality-based utility of the H.264 temporal and quality scalable video layers. Subsequently, we formulate the scalable video scheduling framework as a Markov decision process (MDP) for long-term average video utility maximization and derive the optimal index based-scalable video scheduling policies ISVP and ISVPF towards video quality maximization. Further, we extend this framework to multiuser and multisubchannel scenario of 4G wireless networks. In this context, we propose two novel schemes for long-term streaming video quality performance optimization based on maximum weight bipartite and greedy matching paradigms. Simulation results demonstrate that the proposed algorithms achieve superior end-user video experience compared to competing scheduling policies such as Proportional Fairness (PF), Linear Index Policy (LIP), Rate Starvation Age policy (RSA), and Quality Proportional Fair Policy (QPF).