This paper proposes a joint channel and data estimation (JCDE) algorithm for uplink multiuser extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. The initial channel estimation is formulated as a sparse reconstruction problem based on the angle and distance sparsity under the near-field propagation condition. This problem is solved using non-orthogonal pilots through an efficient low complexity two-stage compressed sensing algorithm. Furthermore, the initial channel estimates are refined by employing a JCDE framework driven by both non-orthogonal pilots and estimated data. The JCDE problem is solved by sequential expectation propagation (EP) algorithms, where the channel and data are alternately updated in an iterative manner. In the channel estimation phase, integrating Bayesian inference with a model-based deterministic approach provides precise estimations to effectively exploit the near-field characteristics in the beam-domain. In the data estimation phase, a linear minimum mean square error (LMMSE)-based filter is designed at each sub-array to address the correlation due to energy leakage in the beam-domain arising from the near-field effects. Numerical simulations reveal that the proposed initial channel estimation and JCDE algorithm outperforms the state-of-the-art approaches in terms of channel estimation, data detection, and computational complexity.

This paper proposes a novel Bayesian reciprocity calibration method that consistently ensures uplink and downlink channel reciprocity in repeater-assisted multiple-input multiple-output (MIMO) systems. The proposed algorithm is formulated under the minimum mean-square error (MMSE) criterion. Its Bayesian framework incorporates complete statistical knowledge of the signal model, noise, and prior distributions, enabling a coherent design that achieves both low computational complexity and high calibration accuracy. To further enhance phase alignment accuracy, which is critical for calibration tasks, we develop a von Mises denoiser that exploits the fact that the target parameters lie on the circle in the complex plane. Simulation results demonstrate that the proposed MMSE algorithm achieves substantially improved estimation accuracy compared with conventional deterministic non-linear least-squares (NLS) methods, while maintaining comparable computational complexity. Furthermore, the proposed method exhibits remarkably fast convergence, making it well suited for practical implementation.

This paper proposes a low-complexity, machine learning (ML)–aided channel denoising framework that applies element-wise filtering in the beam–delay domain to enhance multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) channel estimation. In new radio (NR), demodulation reference signals (DMRSs) enable direct estimation of a subset of the channel state information (CSI) and the remaining CSI is reconstructed by interpolation. However, the noise from reference-based direct estimation can degrade overall accuracy. To address this, we introduce two compact neural network architectures: a shallow single-stream model and a dual-stream factorized model, both built from linear layers, complex domain rectified linear unit (cReLU) activations, and a custom normalization-threshold function. Under the 3GPP UMi channel model, extensive simulations demonstrate that our denoisers outperform classical beam–delay thresholding and a conventional convolutional neural network (CNN)-based method in normalized mean square error (NMSE) and computational cost. Specifically, the proposed designs reduce floating-point operations (FLOPs) by over 95% compared to the CNN benchmark while achieving more than 10% relative NMSE improvements.

Channel denoising plays a critical role in enabling accurate channel estimation for modern multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) systems. As antenna counts and frequency bands proliferate, channel impulse responses become increasingly complex, challenging conventional denoising methods. Motivated by the success of data-driven techniques, we introduce a novel, fully complex-valued transformer architecture tailored for beam–delay domain channel denoising. Key innovations include an inverse exponential positional embedding that avoids corrupting dominant delay taps and an encoder-only design that streamlines one-to-one mapping from noisy to clean channel matrices. The network is trained in a supervised fashion to minimize mean square error (MSE) loss function. Simulation results using 3GPP Urban Micro channel model at 3.5 GHz carrier frequency demonstrate that the proposed framework reduces mean estimation error compared to the legacy threshold-filter method and recent state-of-the-art machine learning (ML)-based denoisers, by a significant margin at 44% and 33%, respectively.

This paper investigates the beamforming design for an upper mid-band multi-user multiple-input single-output (MUMISO) system, aiming to mitigate interference from terrestrial base stations to satellites. The upper mid-band (7-24 GHz) is already utilized by several satellite services, necessitating the coexistence of emerging cellular systems with these existing satellite systems. As such, we propose a beamforming design with an angular radiated power constraint directed towards the sky angle region, effectively mitigating interference without requiring prior information about the satellites. Numerical results validate the efficacy of the proposed method in reducing interference. Moreover, we demonstrate that the proposed method can achieve sum spectral efficiency performance comparable to sum rate maximization approaches without such constraints.

In this paper, we propose a reduced-complexity detector for non-coherent communications with the Grassmann constellation, which enables joint channel and data estimation. Here, we employ deep learning techniques with the aim of reducing computational complexity while maintaining nearly the same channel estimation accuracy as the conventional detector. The conventional maximum likelihood detection is a discrete optimization problem, with complexity increasing exponentially with respect to the transmission rate. In our approach, the Grassmann constellation is detected using a neural network model trained with random signal-to-noise ratios and the corresponding received signals as inputs, and the detected codeword is used for channel estimation. Our simulations demonstrate that the channel estimation accuracy can be maintained with lower complexity compared to the conventional detector, at the cost of a slightly degraded symbol error rate performance. It was also found that the detection complexity can be reduced when the number of receive antennas exceeds four, which is practically relevant.

In this paper, we analyze the performance of the data-carrying reference signal (DC-RS) in time-varying channels. In such scenarios, the spectral efficiency may be reduced because more reference signals need to be transmitted frequently to maintain high channel estimation accuracy. DC-RS has the potential to boost spectral efficiency, which conveys additional data with noncoherent detection, but it has not been analyzed in realistic time-varying channels. By regarding a channel coefficient varying with first-order autoregressive model as an additive independent Gaussian noise for each time slot, we derive the average mutual information of DC-RS. Although the time-varying nature induces performance penalty in general, our numerical simulations demonstrate that the spectral efficiency improves even in rapidly varying channels compared to the case with conventional reference signals, and this trend remains valid upon increasing the mobile speed from 0 to 300 km/h.

In wireless networks, frequent reference signal transmission for accurate channel reconstruction may reduce spectral efficiency. To address this issue, we consider to use a data-carrying reference signal (DC-RS) that can simultaneously estimate channel coefficients and transmit data symbols. Here, symbols on the Grassmann manifold are exploited to carry additional data and to assist in channel estimation. Unlike conventional studies, we analyze the channel estimation errors induced by DC-RS and propose an optimization method that improves the channel estimation accuracy without performance penalty. Then, we derive the achievable rate of noncoherent Grassmann constellation assuming discrete inputs in multi-antenna scenarios, as well as that of coherent signaling assuming channel estimation errors modeled by the Gauss-Markov uncertainty. These derivations enable performance evaluation when introducing DC-RS, and suggest excellent potential for boosting spectral efficiency, where interesting crossings with the non-data carrying RS occurred at intermediate signal-to-noise ratios.

We propose a novel joint channel tracking and data detection (JCTDD) scheme to combat the channel aging phenomenon typical of millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication systems in high-mobility scenarios. The contribution aims to significantly reduce the communication overhead required to estimate time-varying mmWave channels by leveraging a Bayesian message passing framework based on Gaussian approximation, to jointly perform channel tracking (CT) and data detection (DD). The proposed method can be interpreted as an extension of the Kalman filter-based two-stage tracking mechanism to a Bayesian bilinear inference (BBI)-based joint channel and data estimation (JCDE) framework, featuring the ability to predict future channel state information (CSI) from both reference and payload signals by using an auto-regressive (AR) model describing the time variability of mmWave channel as a state transition model in a bilinear inference algorithm. The resulting JCTDD scheme allows us to track the symbol-by-symbol time variation of channels without embedding additional pilots, leaving any added redundancy to be exploited for channel coding, dramatically improving system performance. The efficacy of the proposed method is confirmed by computer simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also approaches the performance of an idealized Genie-aided scheme.

In this paper, a novel radio unit (RU) uplink (UL)/downlink (DL) configuration algorithm for dynamic time division duplex (DTDD) distributed multiple-input multiple-output (D-MIMO) systems with half-duplex RUs is proposed, where the configuration is performed only by the long-term channel statistics without the knowledge of instantaneous channel state information (CSI). Although there are several RU configuration approaches based on instantaneous CSI in the literature, a remaining challenge is to reduce the frequency of RUs UL/DL reconfiguration so as to lower the computational cost in the network and reduce the control signaling overhead in the fronthaul links. The proposed method is shown to be effective compared to the state-of-the-art methods in the trade-off between performance, complexity, and fronthaul overhead.

A novel amplification and phase optimization method for distributed reconfigurable repeater-assisted multiple-input multiple-output (MIMO) systems is proposed in this paper. Although distributed multiple-input multiple-output (D-MIMO) architectures such as cell-free multiple-input multiple-output (CF-MIMO) has shown in the literature to be a promising alternative to the current central multiple-input multiple-output (C-MIMO) deployed in real networks, there remain several challenges to be concured when it comes to real-world deployment. This paper intends to elucidate potential performance of repeater-assisted MIMO systems so as to highlight its capability to be a great successor to C-MIMO in a scenario where/when D-MIMO deployment is arduous. Simulation results are offered to illustrate the effectiveness of the proposed approach compared to other possible MIMO architectures.

We propose a wireless localization algorithm based on complex-domain super multidimensional scaling (CD-SMDS) augmented with a self-localization (SL) component, whereby each target tracks its own motion by incorporating bearing information, obtained e.g., from integrated inertial sensors. The proposed method improves localization accuracy by simultaneously using the time series information of distance and angle associated to the SL information in order to construct the SMDS rank-one edge kernel matrix, maximizing the noise reduction effect of the low-rank truncation via singular value decomposition (SVD). The efficacy of the proposed method over the original CD-SMDS is confirmed via software simulations, and compared with an SL-aware Cramér-Rao lower bound (CRLB).

In this paper, we propose an accurate channel estimation scheme for orthogonal time frequency space modulation, in which we embed multiple superimposed pilots (SPs) instead of a single SP used in the conventional scheme. Multiple SPs are used to mitigate data-pilot interference efficiently and improve the accuracy of channel estimation. Our numerical simulations demonstrate that the proposed scheme outperforms the conventional SP-based scheme in terms of bit error rate and normalized mean squared error of channel estimates, indicating the potential for further improvement in spectral efficiency.

We propose a novel joint channel and data estimation (JCDE) scheme to combat the rate limitation in fronthaul links of cell-free massive MIMO (CF-mMIMO) systems introduced by the use of analog-to-digital converters (ADCs) at access points (APs), which makes channel estimation and multi-user detection at the central AP (CAP) challenging. The latter problem is solved here via the new JCDE scheme which differs from state-of-the-art (SotA) alternatives due to two contributions. The first is the design and incorporation of de-quantization (DQ) step which relies only on scalar Gaussian approximation (SGA) assumptions in conformity with mild central limit theorem (CLT), in contrast to the much harder asymptotic conditions required by the classic bilinear generalized approximate message passing (BiGAMP) algorithm. The second is a modification of bilinear Gaussian belief propagation (BiGaBP), whereby quantized outputs are linearized via the Bussgang decomposition enabling tractable signal processing. The resulting DQ-aided JCDE method achieves both low-complexity and high-accuracy by exploiting both the spatial degrees of freedom (DoF) obtained from, and the observations at the CAP to compensate for the low-resolution distortion introduced by, the distributed APs. The efficacy of the proposed method over the SotA is confirmed via computer simulations.

We propose a new grant-free non-orthogonal multiple access (GF-NOMA) scheme, which makes full use both of the time and of the delay domains for further enhancement on low-latency and massive connectivity. The proposed GF-NOMA is based on a tailored signal model involving the delay-domain channel sparsity in conjunction with phase transition analyses for approximate message passing (AMP) algorithms. Moreover, the proposed design enables efficient and accurate estimation by appropriately designing message-passing rules. Simulation results are offered to demonstrate the superiority of the proposed GF-NOMA approach over the conventional scheme.

In this paper, we propose a novel joint activity and channel estimation (JACE) algorithm for grant-free extra large MIMO (XL-MIMO) systems subject to spatial non-stationarity phenomena by means of a Bayesian bilinear inference framework. In XL-MIMO systems, the signal from each user is visible only by a small portion of its antenna arrays, which are typically distributed over the surface of a certain structure. The sporadic user activity due to grant-free access, as well as the spatial non-stationarity, jointly imposes a challenging JACE problem involving a nested Bernoulli-Gaussian random variable. In order to address this issue, we decompose the latter into a bilinear inference problem of two independent random quantities, deriving novel message passing rules based on Gaussian approximation and bilinear inference. Performance evaluation via software simulations is offered to demonstrate the effectiveness of the proposed algorithm, which achieves the Genie-aided ideal estimation performance.

Extra large MIMO (XL-MIMO) systems are subject to spatial non-stationarity forming visibility regions (VRs), which leads to a sub-array-wise sparse structure of the channel matrix. When XL-MIMO systems operate in grant-free access mode, in which only a fraction of the potential users are active during a given time slot, it follows that the channel matrix possesses a doubly-sparse and user-specific structure such that the activity of each user and each sub-array can be jointly modeled by a nested Bernoulli-Gaussian distribution. This article considers the joint activity and channel estimation (JACE) problem in XL-MIMO systems subject to this so-defined spatial non-stationarity, tackling this challenging inference problem. Our main contributions are 1) to introduce the novel Bernoulli-Gaussian model to simultaneously capture the aforementioned two distinct structured sparsities, and 2) a new bilinear Bayesian inference algorithm capable of jointly estimating the associated channel coefficients, user activity patterns, sub-array activity patterns ($a.k.a$. spatial non-stationarity), boosted by expectation maximization (EM)-based auto-parameterization. In addition, to shed light on a realistic modeling of VRs, we also introduce a Matérn-cluster point process (MCPP)-based approach to imitate the clustered activity pattern due to spatial non-stationarity. The efficacy of the proposed bilinear JACE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also can reach the performance of a genie-aided scheme over wide signal-to-noise-ratio (SNR) ranges, in both uniformly-random and MCPP-based sub-array activity scenarios.

We consider quadrature spatial modulation (QSM) schemes, which achieve high spectral efficiency (SE) via the dispersion of a relatively small number$P$of$M$-ary modulated symbols over a large number of combinations of$n_{T}$transmit antennas and$T$transmit instances. In particular, we design a new space-time block code (STBC)-based scalable QSM scheme combining high SE with maximum diversity and optimum coding gains. Deriving a closed-form expression for the optimum SE, we show that scaling the size$T$with$n_{T}$not only is required to achieve SE optimality, but also results in further gains in bit error rate (BER) performance. Building on the latter optimal parameterization, a fully optimized scalable QSM (OS-QSM) transmitter design is then obtained by introducing a new dispersion matrix index selection algorithm that ensures even utilization of spatial-temporal resources. Finally, a new greedy boxed iterative shrinkage thresholding algorithm (GB-ISTA) QSM receiver is proposed, which exploits the inherent sparsity of QSM signals and while detecting spatially and digitally modulated bits in a greedy fashion. The resulting low complexity of the new receiver, which is linear on$n_{T}$, enables the utilization of OS-QSM in systems of previously prohibitive dimensions.

In this work, we study a full-duplex (FD) cloud radio access network (C-RAN) from the aspects of infrastructure sharing and information secrecy, where the central unit utilizes FD remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., the untrusted RUs. Furthermore, the communication takes place in the presence of untrusted external receivers, i.e., eavesdropper nodes. The communicated uplink (UL) and downlink (DL) waveforms are quantized in order to comply with the limited capacity of the fronthaul links. In order to provide information secrecy, we propose a novel utilization of the quantization noise shaping in the DL, such that it is simultaneously used to comply with the limited capacity of the fronthaul links, as well as to degrade decoding capability of the individual eavesdropper and the untrusted RUs for both the UL and DL communications. In this regard, expressions describing the achievable secrecy rates are obtained. An optimization problem for jointly designing the DL and UL quantization and precoding strategies are then formulated, with the purpose of maximizing the overall system weighted sum secrecy rate. Due to the intractability of the formulated problem, an iterative solution is proposed, following the successive inner approximation and semi-definite relaxation frameworks, with convergence to a stationary point. Numerical evaluations indicate a promising gain of the proposed approaches for providing information secrecy against the untrusted infrastructure nodes and/or external eavesdroppers in the context of FD C-RAN communications.

We propose a novel joint activity, channel and data estimation (JACDE) scheme for multiple-input multiple-output (MIMO) systems. The contribution aims to allow significant overhead reduction of MIMO systems by enabling grant-free access, while maintaining moderate throughput per user. To that end, we extend the conventional MIMO transmission framework so as to incorporate activity detection capability without resorting to spreading informative data symbols, in contrast with related work which typically relies on signal spreading. Our method leverages a Bayesian message passing scheme based on Gaussian approximation, which jointly performs active user detection (AUD), channel estimation (CE), and multi-user detection (MUD), incorporating also a well-structured low-coherence pilot design based on frame theory, which mitigates pilot contamination, and finally complemented with a detector empowered by bilinear message passing. The efficacy of the resulting JACDE-based grant-free access scheme in the cell-free MIMO system setup compliant with fifth generation (5G) new radio (NR) orthogonal frequency-division multiplexing (OFDM) signaling is demonstrated by simulation results. The results are shown to outperform the current state-of-the-art and approach the performance of an idealized (genie-aided) scheme in which user activity and channel coefficients are perfectly known.

We consider a bidirectional in-band full-duplex (FD) multiple-input multiple-output (MIMO) system subject to imperfect channel state information (CSI), hardware distortion, and limited analog cancellation capability as well as the selfinterference (SI) power requirement at the receiver analog domain so as to avoid the saturation of low noise amplifier (LNA). A novel minimum mean square error (MMSE)-based joint design of digital precoder and combiner for SI cancellation is offered, which combines the well-known gradient projection method and non-monotonicity considered in recent machine-learning literature in order to tackle the non-convexity of the optimization problem formulated in this article. Simulation results illustrate the effectiveness of the proposed SI cancellation algorithm.

Full-Duplex (FD) Amplify-and-Forward (AF) Multiple-Input Multiple-Output (MIMO) relaying has been the focus of several recent studies, due to the potential for achieving a higher spectral efficiency and lower latency, together with the inherent processing simplicity. However, when the impact of hardware distortions is considered, such relays suffer from a distortion-amplification loop, due to the inter-dependent nature of the relay transmit signal covariance and the residual self-interference covariance. The aforementioned behavior leads to a significant performance degradation for a system with a low or medium hardware accuracy. In this work, we analyse the relay transfer function as well as the Mean Squared- Error (MSE) performance of an FD-AF MIMO relay-assisted communication, under the consideration of collective sources of additive and multiplicative transmit and receive impairments. An optimization problem is then devised over the linear transmit and receive strategies to minimize the communication MSE and solved by employing the recently proposed Penalty Dual Decomposition (PDD) method. The proposed solution converges to a stationary point of the original problem via a sequence of quadratic convex programs. Numerical simulations verify the significance of the proposed distortion-aware design compared to the common simplified approaches, as the hardware accuracy degrades.

We address a bi-directional full-duplex (FD) multiple-input multiple-output (MIMO) system equipped with limited capability for analog self-interference cancellation (SIC) and subjected to hardware (HW) impairments and imperfect channel state information (CSI) at the nodes. We propose an alternating algorithm to minimize transmit (TX) power subject to quality of service (QoS) guarantees in such systems, where the signal to interference-plus-noise ratio (SINR) constraint is relaxed via a Fractional Programming (FP) approach so that optimal TX beamforming vectors can be obtained using standard convex optimization tools. Simulation results show that the proposed algorithm significantly reduces the required TX power while outperforms not only a conventional zero-forcing (ZF) scheme but also the State-of-the-Art (SotA) method in terms of outage probabilities of the prescribed SINRs.

We present a new Beamforming-based (BB) Multiple-Input Single-Output (MISO)- Non -orthogonal Multiple Access (NOMA) scheme for Power Domain NOMA (PD-NOMA), in which the total transmit power consumption is minimized subjected to prescribed signal-to-interference-plus-noise ratio (SINR) requirements for each user, and under the assumption that only imperfect channel state information (CSI) is available at the transmitter. To this end, the fractional programming (FP)-based quadratic transform is employed to reformulate the non-convex SINR constraint of the original problem into a tractable quadratic form, which contains an estimate of the CSI error vector as a parameter. Taking advantage of the fact that the zero duality gap holds for the non-convex quadratic problems, a closed-form expression for an estimate of the CSI error vector is derived, completing the formulation. Finally, a novel iterative algorithm based on both the herein derived CSI error vector and the semidefinite relaxation (SDR) technique is contributed, which is shown to capable of efficiently solving the constrained min-power problem. Simulation results are given which illustrate the effectiveness of the proposed algorithm, which is found to sacrifice only small quantities of transmit power in return for substantial increase in robustness against CSI imperfection.

We study a multiple-input multiple-output (MIMO) full-duplex (FD) radio system, aiming to increase the feasibility of this technology in bi-directional communications. In particular, we consider that the FD radios are equipped with the State-of-the-Art (SotA) hybrid SI cancellation (SIC) capabilities, but must cope with hardware (HW) and channel state information (CSI) imperfections, contributing four new MIMO beamforming (BF) schemes for such systems. The first is a transmit (TX) beamforming scheme designed via a Fractional Programming (FP) approach, matched with a minimum mean square error (MMSE) beamformer at the receiver. In this benchmark, the FP-based method, the signal to interference-plus-noise ratio (SINR) constraints are relaxed via the quadratic transform (QT), which allows for the SINR-constrained TX-power minimization problem to be solved using interior point methods. Motivated by the high complexity of the latter, three low-complexity alternatives are then derived, in which power minimization is performed via the Perron-Frobenius (PF) approach, while the TX-BF vectors are obtained, respectively, via direct Gradient Projection (GP), QT-relaxation, and via a Double Rayleigh Quotient (DRQ) reformulation of the original optimization problem. The simulation results confirm the significant gains achieved by all four schemes over the SotA, revealing the GP and DRQ as the overall best alternatives depending on power limitation, and HW/CSI qualities.

We propose a new scheme for the robust estimation of the millimeter wave (mmWave) channel. Our approach is based on a sparse formulation of the channel estimation problem coupled with a frame theoretic representation of the sensing dictionary. To clarify, under this approach, the combined effect of transmit precoders and receive beamformers is modeled by a single frame, whose design is optimized to improve the accuracy of the sparse reconstruction problem to which the channel estimation problem is ultimately reduced. The optimized sensing dictionary frame is then decomposed via a Kronecker decomposition back into the precoding and beamforming vectors used by the transmitter and receiver. Simulation results illustrate the significant gain in estimation accuracy obtained over state of the art alternatives. As a bonus, the work offers new insights onto the sparse mmWave-multiple-input multiple-output (MIMO) channel estimation problem by casting the trade-off between correlation and variation range in terms of frame coherence and tightness.

In the traditional model of wireless communications systems, additive white Gaussian noise (AWGN) is assumed to be linear. However, in an increasingly important class of emerging communication systems — e.g. device-to-device (D2D), full-duplex (FD) and low-cost Internet-of-things (IoT) communication systems — the nonlinearity caused by factors such as device proximity (D2D), residual self-interference (FD) and imperfect power amplification (IoT) can no longer be neglected. A possible mechanism to improve the performance of such systems is to optimize the transmit constellation utilized, which is known in the literature as constellation shaping. With that in mind, we propose a probabilistic constellation shaping scheme to maximize the achievable rates of communication systems affected by nonlinear AWGN. To this end, we derive the analytical expression of the mutual information (MI) of such nonlinear additive white Gaussian noise (AWGN) systems with arbitrary modulation, and maximize the latter by numerically optimizing the corresponding channel input distribution. The result is a semi-analytical scheme (with analytical objective optimized numerically) which are shown to outperform systems employing conventional (uniformly distributed) constellations.