We propose a framework to design integrated communication and computing (ICC) receivers capable of simultaneously detecting data symbols and performing over-the-air computing (AirComp) in a manner that: a) is systematically generalizable to any nomographic function, b) scales to a massive number of user equipments (UEs) and edge devices (EDs), c) supports the computation of multiple independent functions (streams), and d) operates in a multi-access fashion whereby each transmitter can choose to transmit either data symbols, computing signals or both. For the sake of illustration, we design the proposed multi-stream and multi-access method under an uplink setting, where multiple single-antenna UEs/EDs simultaneously transmit data and computing signals to a single multiple-antenna base station (BS)/access point (AP). Under the communication functionality, the receiver aims to detect all independent communication symbols while treating the computing streams as aggregate interference which it seeks to mitigate; and conversely, under the computing functionality, to minimize the distortion over the computing streams while minimizing their mutual interference as well as the interference due to data symbols. To that end, the design leverages the Gaussian belief propagation (GaBP) framework relying only on element-wise scalar operations coupled with closed-form combiners purposebuilt for the AirComp operation, which allows for its use in massive settings, as demonstrated by simulation results incorporating up to 200 antennas and 300 UEs/EDs. The efficacy of the proposed method under different loading conditions is also evaluated, with the performance of the scheme shown to approach fundamental limiting bounds in the under/fully loaded cases.

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.

We propose a quantum-assisted solution of the pilot assignment problem. In particular, we formulate a pilot assignment problem designed to minimize the interference from co-pilot user terminals as a binary optimization problem which can be solved via a quantum exhaustive search through the Grover adaptive search (GAS) algorithm. The performance of GAS in terms of query complexity is enhanced by introducing a modified initial state and an improved initial threshold using the upper bound for the minimum value of the objective function. Simulation results demonstrate that our proposed method can provide quadratic speedup compared to an exhaustive search performed by a classical computer, concretely demonstrating that quantum computers can be employed to solve optimally and efficiently the pilot assignment problem.

We propose a novel method for blind bistatic radar parameter estimation (RPE), which enables integrated sensing and communications (ISAC) by allowing passive (receive) base stations (BSs) to extract radar parameters (ranges and velocities of targets), without requiring knowledge of the information sent by an active (transmit) BS to its users. The contributed method is formulated with basis on the covariance of received signals, and under a generalized doubly-dispersive channel model compatible with most of the waveforms typically considered for ISAC, such as orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS) and affine frequency division multiplexing (AFDM). The original non-convex problem, which includes an ℓ0-norm regularization term in order to mitigate clutter, is solved not by relaxation to an ℓ1-norm, but by introducing an arbitrarily-tight approximation then relaxed via fractional programming (FP). Simulation results show that the performance of the proposed method approaches that of an ideal system with perfect knowledge of the transmit signal covariance with an increasing number of transmit frames.

We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency division mul-tiplexing (OFDM) systems via optimized sparsification of tone reservation (TR) aimed at freeing resources for the opportunistic operation of co-existing communication and sensing systems. In particular, we propose a novel TR-optimization method in which the minimum number of effectively used peak-reserved tones (PRTs) required to satisfy a prescribed PAPR level in a primary system is found, leaving the remaining resources free to be opportunistically allocated by a secondary system and other functionalities, such as joint communication and sensing (JCAS), index modulation (IM) and cognitive radio (CR). The proposed method relies on an £0 norm regularization approach to penalize the number of PRTs, leading to a problem convexized via fractional programming (FP), whose solution is shown to ensure that the prescribed PAPR is achieved with high probability with a smaller number of PRTs than state of the art (SotA) methods. The contribution can be seen as a mechanism to enable the opportunistic coexistence of systems with adjacent functionalities in presence of existing OFD M - based systems.

We consider the design of low complexity and highperforming mean square error (MSE) minimization combiners for over-the-air-computing (AirComp) applications operating over the uplink of a system with one multiple-antenna access point (AP) and multiple single-antenna edge devices (EDs). Within that paradigm, we offer two contributions, namely, a simple initial combiner based on a Rayleigh quotient (RQ) design, and a low-complexity refinement stage based on a convex concave procedure (CCP). The new refinement stage algorithm is further enriched with an efficient (offline) hyper-parameter tuning mechanism via Bayesian optimization (BO) and acceleration method based on a half-space constrained least square problem reformulation solved via the adaptive moment estimation (Adam) algorithm. The low complexity and good performance of the proposed method help address typical limitations of edge devices. Numerical results demonstrate that the proposed design can achieve MSE performances equivalent to those of the best stateof-the-art (SotA) alternatives currently known, at about 200-times less complexity than the highest-performing SotA, and about 4-times less complexity than its low-complexity counterpart.

We propose a new two-stage, low-complexity, and robust beamforming (BF) method for heterogeneous MIMO rate splitting multiple access (RSMA) systems. In the proposed method, the phases and powers of the BF weights are designed separately, the first based on a tensor factorization of the channels between the base station (BS) and each user, and the second based on a fractional programming (FP) formulation of the power allocation problem, which is offered in three distinct variations, aimed as sum rate maximization (SRM), minimum rate maximization (MaxMin) and the maximization of the geometric-mean (GMean) of achievable rates, respectively. Thanks to the twostage approach, the proposed method is capable of delivering robustness to both channel state information (CSI) and successive interference cancellation (SIC) errors (incorporated in the phase design), at a low complexity compared to state-of-the-art (SotA) alternatives. Also thanks to the approach, the scheme naturally handles heterogeneity in terms of the number of antennas at each user, which can be arbitrarily distinct. Direct comparisons between SotA and the proposed schemes demonstrate that the contributed method generally outperforms the best alternative at comparable complexity, while approaching the best-performing SotA method of significantly higher complexity. In fact, the computational cost advantage of the proposed technique over the latter is quantified analytically and shown to be proportional to the cube of the number of BS antennas.

We consider the joint design of time-domain equalization and hybrid transmit and receive beamforming schemes, so as to combat frequency-selective fading and path loss effects in single-carrier (SC) multiple-input multiple-output (MIMO) communications systems. In particular, considering the linear equalization and hybrid beamforming matrices as variables, a minimum mean square error (MMSE) problem to minimize the bit error rate (BER) of SC-MIMO systems is formulated and solved via an accelerated matrix quadratic transform (QT) and manifold optimization. The relationship between mean square error (MSE) minimization via joint MMSE equalization and beamforming, and the minimization of average BER over all data streams is expressed analytically, which together with numerical results confirm that the proposed SC scheme outperforms the SC systems whose linear equalizer and beamformers are designed separately.

In this paper, we present a novel approach to analog beam selection in cell-free massive multi-input multi-output (CF-mMIMO) systems employing hybrid beamforming (BF) over millimeter-wave (mmWave) channels. In conventional CF-mMIMO systems with hybrid BF, an analog beam is selected by a central processing unit (CPU) from a predefined codebook. This selection is based on the received power at each access point (AP) and the fairness among user equipment (UE). However, this conventional method disregards the potential impact of interference and heavily relies on digital BF to mitigate it. Consequently, when utilizing low-complexity digital BF techniques such as maximum ratio transmission (MRT), the system's performance experiences degradation. Our proposed beam selection method takes into account both the received power and the interference power, effectively mitigating the influence of interference while maintaining an adequate level of received power. The numerical simulations validate the efficacy of our proposed approach.

This paper proposes a scalable network-assisted full-duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) system that achieves high spectral efficiency (SE) while reducing the fronthaul load by forming clusters of access points (APs) exclusively for uplink and downlink based on user equipment (UE) requirements, respectively. Specifically, we propose AP clustering techniques based on convex optimization and Hungarian algorithm, along with downlink transmit power control suitable for proposed AP clustering. Numerical results demonstrate that our proposed approach achieves higher SE than conventional scalable TDD CF-mMIMO, NAFD CF-mMIMO and small cell with dynamic time division duplex (TDD), under fronthaul capacity limitations.

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.

The availability of multi-view images of a scene makes new and exciting applications possible, including Free-Viewpoint TV (FTV). FTV allows us to change viewpoint freely in a 3D world, where the virtual viewpoint images are synthesized by Image-Based Rendering (IBR). In this paper, we introduce a FTV depth estimation method for forward virtual viewpoints. Moreover, we introduce a view generation method by using a zoom camera in our camera setup to improve virtual viewpoint-ts' image quality. Simulation results confirm reduced error during depth estimation using our proposed method in comparison with conventional stereo matching scheme. We have demonstrated the improvement in image resolution of virtually moved forward camera using a zoom camera setup.