Fifth-generation millimeter wave (mmWave) systems rely on sub-connected (SC) hybrid beamforming architecture due to low power consumption and reduced hardware complexity. In SC architecture, each RF chain is connected to a subset of antennas which limits the associated beamforming gain. Thus, designing beam patterns for SC mmWave systems that can effectively serve multiple users with high beamforming gains becomes a challenging task. Considering a clustered user geometry to model multiple users in close proximity, this work proposes a beam pattern multiplication-based precoder design to improve the overall spectral efficiency performance in mmWave systems. Simulation results demonstrate the effectiveness of the proposed design for SC mmWave systems in terms of high beamforming gain, improved spectral and energy efficiency.

The amalgamation of millimeter-wave (mmWave) communications and multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) systems holds significant promise for next-generation wireless networks, offering high data rates and robust performance in high-mobility environments. This paper presents a novel convolutional neural network (CNN)-based channel estimation framework that unfolds the sparse Bayesian learning (SBL) algorithm into a deep neural network (DNN) for mmWave MIMO-OTFS systems. The proposed SBL-based Deep CNN (SBL-DCNN) is a modeldriven approach that combines the conventional expectation maximization (EM)-based SBL algorithm with a novel sparsity mask feature tailored exclusively for the delay Doppler domain (DD) to promote sparsity in channel predictions and accelerate the convergence of the training stage. This hybrid strategy improves MIMO-OTFS channel estimation by combining the strengths of SBL and domain-specific features, employing a cascade of diverse 2D convolution filters to effectively capture the complex underlying channel sparsity structures. The proposed network trained on data at moderate signal-to-noise power ratio (SNR) values demonstrates strong generalization and improved prediction performance across a wide range of SNR conditions in comparison to conventional sparse signal recovery-based channel estimation schemes.

This paper proposes novel Bayesian learning approaches for sparse channel estimation in a multi-user millimeter-wave massive multiple-input multiple-output underlay cognitive radio system. The intelligent reflecting surfaces (IRS)-aided secondary network adopts a two-phase transmission protocol comprising of silent and estimation phases. During the silent phase, the secondary base station(SBS) captures primary network pilot transmissions to estimate the cascaded channel between primary users and the SBS. Next, the estimation phase considers two pilot design policies with an inherent estimation accuracy and spectral efficiency trade-off, for cascaded channel estimation with respect to the secondary users, IRS, and SBS. Further, the associated hybrid and marginalized Cramér-Rao bounds are developed to benchmark the efficacy of proposed estimation schemes. Simulation results demonstrate the superior performance of the proposed approaches in comparison to existing compressed sensing methods such as orthogonal matching pursuit and subspace multi-user joint channel estimation.

Intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) systems are potential contenders for large scale deployment in 5G and beyond communication. Owing to the passive nature of IRS systems, the acquisition of accurate channel state information in bandwidth constrained scenarios becomes a highly challenging task. This work leverages the inherent sparsity associated with cascaded channels of IRS mmWave systems to develop novel sparse Bayesian learning (SBL)-based channel estimation algorithms. The underlying row-wise temporal correlation of the effective angular domain sparse channel matrix is first theoretically demonstrated followed by the development of the temporal SBL channel estimation approach. Further, utilizing the scaling property of cascaded channels for multiple users, low complex variants of the proposed SBL solutions are developed. Simulation results demonstrate significant improvement of the proposed SBL schemes over existing techniques.

Fifth-generation millimeter wave (mmWave) systems deploy spatial beam search using synchronization signal (SS) blocks for initial access (IA). Technique based on narrow beam sweeping procedure completed in a single SS burst is optimum for idle users that have arrived before IA. With a maximum IA duration of 5ms specified by 3GPP Release 15, the probability of user arrival during IA is non-trivial. For these users, existing search procedures will result in significant discovery delays and low detection probability. This work considers an alternative scenario wherein the user arrives after the commencement of IA. In such cases, a single SS burst may not suffice and one must consider repeating the SS burst to enhance user detection probability and reduce IA delay. Repetition of SS burst brings the trade-off between half-power beamwidth (HPBW) and the number of SS blocks per burst i.e., large HPBW supports a smaller number of SS blocks per burst and vice-versa. To achieve high accuracy of angular position with SS burst repetition, a grating-based search has been proposed which utilizes antenna element spacing greater than half-wavelength. This paper presents an IA delay, location error and detection analysis for both scenarios; with and without SS burst repetition. Simulation results demonstrate the enhanced performance of the proposed grating search technique with SS burst repetition in terms of user detection, reduced IA delay, and low angular position error.

In the channel code detection problem, given a sequence of noise-affected codewords generated by an unknown code, the aim is to identify the correct channel code from the given set of potential codes. This problem has many applications in military spectrum surveillance and in cognitive radios. In this paper, we consider the situation when the set of potential channel codes consists of two linear block codes and propose a deep learning based classification approach for the corresponding code detection problem. In this work, we propose data processing strategies suitable for linear block codes that reduce the amount of data required to train the deep neural network classifier. This however comes at the cost of having a lower probability of detection. For the proposed data processing strategies, we analytically obtained the optimal probability of correct detection that one can hope to achieve using any neural network classifier. The proposed results are validated numerically using a variety of examples.

Orthogonal frequency division multiplexing (OFDM)-based internet of things (IoT) networks undergo severe nonlinear distortion at the transmitter side owing to saturation of the high power amplifiers. Compensation of RF impairments at the receiver requires precise knowledge of the clipping amplitude level which in turn necessitates an accurate channel estimate. This paper leverages the inherent temporal sparsity associated with frequency selective wireless channels to develop a novel joint sparse channel and clipping amplitude estimation framework based on the popular sparse Bayesian learning (SBL) algorithm. The proposed scheme iteratively obtains the maximum likelihood estimate of the clipping amplitude followed by an expectation maximization (EM)-based sparse channel vector estimate. Numerical simulations are demonstrated to validate the superiority of the proposed technique over a sparsity agnostic scheme in terms of mean squared error (MSE) of channel, clipping amplitude estimates and symbol error rate (SER).

Massive multiple-input multiple-output (MIMO) systems operating in the millimeter-wave (mmWave) frequency band support extremely high data rates. One of the major shortcoming of severe path losses in these systems is addressed by the employment of large phased array antennas with highly directional beams. Owing to the precise nature of the beams, identification of the most suitable beam for link establishment between the base station (BS) and the user equipment (UE) becomes an extremely challenging and time-consuming task. The existing two-level phased array approach is based on an exhaustive search that requires a high number of beam sweeps. In this work, a novel hierarchical fast beam search approach based on a two-level phased array is proposed for improved UE discovery in mmWave massive MIMO systems. The proposed approach leverages suitable antenna spacing and radiation pattern multiplication at two-levels to yield refined beams. The hierarchical implementation via grouped antenna units results in a refinement of the beamspace at the digital level based on the best beam direction evaluated at the analog subarray level. Extensive simulation results validate the superior performance of the proposed algorithm in terms of mean position error and the total number of beam sweeps. The investigation of misdetection probability will be considered in our future work.

Sparse Bayesian learning (SBL)-based channel state information (CSI) estimation schemes are developed for filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). Initially, an SBL-based channel estimation scheme is designed for a frequency-selective quasi-static single-input single-output (SISO)-FBMC system, relying on the interference approximation method (IAM). The IAM technique, although has low complexity, is only suitable for channels exhibiting mild frequency-selectivity. Hence, an alternative time-domain (TD) model based sparse channel estimation framework is developed for highly frequency-selective channels. Subsequently, the Kalman filtering (KF)-based IAM and its TD counterpart are developed for sparse doubly-selective CSI estimation in SISO-FBMC systems. These schemes are also extended to FBMC-based multiple-input multiple-output (MIMO) systems, for both quasi-static and doubly-selective channels, after demonstrating the special block and group-sparse structures of the IAM and TD-based models respectively, which are the characteristic features of such channels. The Bayesian Cramér-Rao lower bounds (BCRLBs) and the time-recursive BCRLBs are derived for the proposed quasi-static as well as doubly-selective sparse CSI estimation models, respectively. Our numerical results closely match the analytical findings, demonstrating the enhanced performance of the proposed schemes over the existing techniques.

This paper presents a novel random parameter-based formulation of the expectation maximization (EM) framework towards joint symbol detection and channel estimation over doubly selective channels in block transmission systems. A key feature of the proposed approach is that it is developed for a generic block transmission system, and is thus applicable for a wide variety of systems such as cyclic prefix(CP)/ zero-padding (ZP) single carrier (SC)/ multi carrier (MC) systems, with OFDM, SC-FDMA etc. as special cases. The proposed EM approach leads to a minimum mean squared error (MMSE)based data aided Kalman filter and smoother (KFS) in the E-step followed by the symbol vector estimate in the M-step. Further, the Bayesian Cramér-Rao bound (BCRB) is derived to characterize the mean squared error (MSE) performance of the proposed scheme. Simulation results are presented to demonstrate the performance of the proposed technique and validate the analytical bounds.

This paper presents a novel application of various machine learning (ML)-based approaches towards prediction of path loss (PL) parameter for a smart campus environment. Measured data from [1] are used to train and evaluate the performance of popular ML techniques such as artificial neural network (ANN) and random forest (RF). Simulation results are presented to verify the PL prediction accuracy of the ML-based schemes. Further, a detailed comparison with the widely used empirical COST-231 Hata model demonstrates the superiority over conventional techniques thereby validating the suitability of employing ML for path loss prediction in challenging 5G wireless scenarios.

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.

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 Cramér-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 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.

This paper proposes a novel sparse Bayesian learning (SBL) framework towards target imaging in monostatic MIMO radar systems. Owing to the improved sparse signal recovery guaranteed by SBL, the proposed SBL-based imaging approach is seen to yield a higher resolution and significantly greater sidelobe suppression in comparison to the existing state-of-the-art non-sparse and sparse imaging techniques. Further, a novel joint SBL-based target imaging and angular Doppler frequency estimation scheme is also developed for scenarios with multiple mobile point targets and unknown angular Doppler frequencies. It is demonstrated that the Doppler frequency estimates can be obtained based on a first order Taylor series expansion of the overcomplete dictionary matrix expressed as a function of the Doppler frequencies. Simulation results are presented to validate the efficacy of the proposed techniques.

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.