Adopting high-frequency bands in 6G networks poses many challenges, including path loss, limited coverage, and blockages. Reconfigurable Intelligent Surfaces (RIS) and passive reflectors offer a potential solution. However, experimental evaluations, especially in FR2 bands, remain scarce. This paper presents a controlled measurement campaign using a Near-Field (NF) probe to assess the bistatic Near-Field-to-Far-Field (NF-FF) reflection patterns of four passive RIS and six passive reflectors, characterizing their performance from 24–42 GHz. Results show that conventional reflectors achieve better Error Vector Magnitude (EVM) and Adjacent Channel Power Ratio (ACPR) than RIS, highlighting limitations in current RIS designs. These findings provide key insights into RIS performance, bridging the gap between theory and real-world deployment for high-frequency communications.

In this paper, we investigate the multi-connectivity for Extended Reality (XR) devices, having both a direct link and an indirect link to the same Next-Generation Node B (gNodeB). The indirect link is established through a cooperating 5th generation (5G) tethering device positioned in proximity to the XR device. Our study focuses on point-to-multipoint (PTM) multicast transmission to the XR device and the nearby 5G tethering device, emphasizing their cooperative interaction. We conduct this investigation under a spatially consistent channel model, quantifying the performance in terms of block error rate (BLER). Furthermore, we propose algorithms for processing joint hybrid automatic repeat request (HARQ) feedback (FB) in 5G multicast sessions. These algorithms are designed for multicast sessions, where User Equipment (UE)s actively cooperate, aiming to enhance the spectral efficiency of multiconnected XR devices by minimizing HARQ retransmissions. The resulting increase in spectral efficiency contributes to the overall capacity improvement of the network. Our link-level simulations demonstrate that multi-connectivity for XR yields a 0.8 dB gain for a 10% BLER target. Additionally, the proposed joint HARQ FB processing algorithm provides approximately a 7% gain in spectral efficiency.

Deep learning (DL) methods have shown potential in tackling the performance-complexity trade-off in multiple-input multiple-output (MIMO) detection. Unlike most studies that evaluate state-of-the-art (SoA) DL receivers in uncoded scenarios, our paper focuses on realistic coded MIMO systems. After a comprehensive literature review, three representative SoA model-based DL receivers viz: DetNet, OAMPNet2, and DUIDD (MMSE-PIC and LoCo-PIC) were selected and comprehensively evaluated. Our findings indicate that DL receivers such as DetNet and OAMPNet2, which base their classical designs on the principle of symbol denoising, fail to sustain their superior performances from uncoded systems to coded systems due to inaccurate residual noise statistics. In contrast, DUIDD, specifically designed for coded systems, achieves effective interference cancellation, resulting in improved coded bit error rates across i.i.d. Gaussian channels, suggesting promising avenues for future research. However, LoCo-PIC, which simplifies MMSE-PIC with a linear solution, suffers performance degradation in correlated urban microcell channels, highlighting the importance of considering the non-linear correlation impacts during detection. Additionally, this simplification leads to further degradation in out-of-distribution channel scenarios, emphasizing the need to address these impacts in realistic wireless systems with varying channel conditions.

Industry 4.0 requires wireless solutions to meet the demanding reliability and latency requirements of cyber-physical systems serving many industrial sensors and actuators. The cell-free massive multiple input multiple output (MIMO) paradigm, exploiting both massive MIMO gains and cell-free properties, is expected to play a crucial role in addressing Industry 4.0 communication requirements. However, finding an optimal user association to satisfy the quality of service (QoS) requirements is critical. In this article, we propose a novel centralized probabilistic user association algorithm to identify and maximize the number of served users without relying on short-term channel state information to satisfy the QoS requirements. In larger factories, approximately 90% of the total users are satisfied. However, as the system becomes interference-limited and is expected to meet challenging requirements, the number of served users decreases. Nevertheless, the proposed solution exhibits adaptability in maximizing the number of satisfied users. Simulation results further prove the effectiveness of the proposed solution in terms of achievable spectral efficiency and maximizing the number of satisfied users across varying requirements and factory sizes.

Time-varying terminal orientation is an often overlooked challenge of beam alignment in millimeter wave communications with multi-panel handset terminals. The use of narrow beams, allied with fast and hard to predict device orientation changes, cause the current measurement-based beam management procedure to rely on potentially outdated beam information, degrading its accuracy. This paper explores the capabilities of deep neural networks to improve user equipment (UE) beam selection under dynamic terminal orientation conditions. Contrary to other works, the proposed solution relies solely on reference signal received power beam measurements, without the aid of other context information. Results show that this simple solution can successfully improve beam selection accuracy for fast rotating UEs, especially in line-of-sight scenarios. No-line-of-sight environments however reduce the proposed solution’s effectiveness due to low power-levels and increased channel angular spread.

While initial beam alignment (BA) in millimeter-wave networks has been thoroughly investigated, most research assumes a simplified terminal model based on uniform linear/planar arrays with isotropic antennas. Devices with non-isotropic antenna elements need multiple panels to provide good spherical coverage, and exhaustive search over all beams of all the panels leads to unacceptable overhead. This paper proposes a location- and orientation-aware solution that manages the initial BA for multi-panel devices. We present three different neural network structures that provide efficient BA with a wide range of training dataset sizes, complexity, and feedback message sizes. Our proposed methods outperform the generalized inverse fingerprinting and hierarchical panel-beam selection methods for two considered edge and edge-face antenna placement designs.

5th Generation (5G) millimeter wave (mmWave) communications are enabled through directive and narrow beams that mitigate these frequencies' challenging propagation conditions. In the future, 5G-Advanced and 6G will go even higher in the frequency spectrum, to allow for progressively larger bandwidths. The need for a larger number of narrower beams will put a strain in the current analog beamforming (BF) based beam management (BM) framework. This paper proposes an alternative signalling method for BM to parallelize the beam sweeping procedure using a hybrid analog-digital (HAD) BF architecture to enable mmWave signal multiplexing with a manageable overhead. The proposed solution is shown to significantly enhance beam alignment performance while reducing signalling overhead and latency.

We study the design of receivers in extra-large scale MIMO (XL-MIMO) systems, i.e. systems in which the base station is equipped with an antenna array of extremely large dimensions. While XL-MIMO can significantly increase the system’s spectral efficiency, they present two important challenges. One is the increased computational cost of multi-antenna processing. The second one is the presence of spatial non-stationarities in the channel response, which imply that the mean energy of a given user’s signal varies across the array. Such non-stationarities limit the performance of the system. In this paper, we propose a distributed receiver for such an XL-MIMO system that can address both challenges. Based on variational message passing (VMP), we propose a set of receiver options providing a range of complexity-performance characteristics to adapt to different requirements. Furthermore, we distribute the processing into local processing units (LPU), that can perform most of the complex processing in parallel, before sharing their outcome with a central processing unit (CPU). Our designs are specifically tailored to exploit the spatial non-stationarities and require lower computations than linear receivers. Our simulation study, performed with a channel model accounting for the special characteristics of XL-MIMO channels, confirms the superior performance of our proposals compared to the state of the art methods.

Position-aided beam selection methods have been shown to be an effective approach to achieve high beamforming gain while limiting the overhead and latency of initial access in millimeter wave (mmWave) communications. Most research in the area, however, has focused on vehicular applications, where the orientation of the user terminal (UT) is mostly fixed at each position of the environment. This paper proposes a location- and orientation-based beam selection method to enable context information (CI)-based beam alignment in applications where the UT can take arbitrary orientation at each location. We propose three different network structures, with different amounts of trainable parameters that can be used with different training dataset sizes. A professional 3-dimensional ray tracing tool is used to generate datasets for an IEEE standard indoor scenario. Numerical results show the proposed networks outperform a CI-aided benchmark such as the generalized inverse fingerprinting (GIFP) method as well as hierarchical beam search as a non-CI-based approach. Moreover, compared to the GIFP method, the proposed deep learning-based beam selection shows higher robustness to different line-of-sight blockage probability in the training and test datasets and lower sensitivity to inaccuracies in the position and orientation information.

Among the new types of connectivity unleashed by the emerging 5G wireless systems, Ultra-Reliable Low Latency Communication (URLLC) is perhaps the most innovative, yet challenging one. Ultra-reliability requires high levels of diversity, however, the reactive approach based on packet retransmission in HARQ protocols should be applied carefully to conform to the stringent latency constraints. The main premise of this paper is that the NOMA principle can be used to achieve highly efficient retransmissions by allowing concurrent use of wireless resources in the uplink. We introduce a comprehensive solution that accommodates multiple intermittently active users, each with its own HARQ process. The performance is investigated under two different assumptions about the Channel State Information (CSI) availability: statistical and instantaneous. The results show that NOMA can indeed lead to highly efficient system operation compared to the case in which all HARQ processes are run orthogonally.

Location-aided beam alignment methods exploit the user location and prior knowledge of the propagation environment to identify the beam directions that are more likely to maximize the beamforming gain, allowing for a reduction of the beam training overhead. They have been especially popular for vehicle to everything (V2X) applications where the receive array orientation is approximately constant for each considered location, but are not directly applicable to pedestrian applications with arbitrary orientation of the user handset. This paper proposes a deep neural network based beam selection method that leverages position and orientation of the receiver to recommend a shortlist of the best beam pairs, thus significantly reducing the alignment overhead. Moreover, we use multi-labeled classification to not only capture the beam pair with highest received strength but also enrich the neural network with information of alternative beam pairs with high received signal strength, providing robustness against blockage. Simulation results show the better performance of the proposed method compared to a generalization of the inverse fingerprinting algorithm in terms of the misalignment and outage probabilities.

The requirement to accommodate ultra-reliable low latency communication (URLLC) is one of the most attractive, yet challenging, new features of upcoming 5G systems. A common way to achieve reliability is retransmission; however, the applicability of this mechanism is hindered by the strict latency requirements. Furthermore, the bandwidth is often limited and shared by multiple connections, which may put the packet into a retransmission queue, leading to even larger latency. We address this problem in an uplink setting by introducing the concept of non-orthogonal multiple access hybrid automatic repeat request (NOMA-HARQ). In essence, NOMA-HARQ allows newly incoming packets to share non-orthogonally the same resource with retransmitted packets. The reliability guarantees are preserved by designing a power optimization procedure that takes into account past transmission attempts as well as the time remaining until the deadline.

We design an algorithm for OFDM receivers operating in co-channel interference conditions, where the serving and interfering transmitters are synchronized in time. The channel estimation problem is formulated as one of sparse signal reconstruction using multiple measurement vectors. The proposed design harnesses the sparse common support of, respectively, the desired and interfering MIMO sub-channels by adopting a Bernoulli-Gaussian prior for the weights of the impulse responses of these sub-channels. Then, applying a variational Bayesian inference method we derive an algorithm that performs joint channel estimation, interference cancellation and decoding. Simulation results show how the performance of the proposed receiver depends on its knowledge of the interfering signals' modulation and code. When these are known, our receiver approaches the performance of a genie-aided receiver with perfect interference cancellation. Even with mismatched assumptions on the modulation, our proposed implementation still outperforms receivers which neglect the interference.

In this article we propose an iterative training scheme that approximates optimal beamforming between two transceivers equipped with hybrid digital-analog antenna arrays. Inspired by methods proposed for digital arrays that exploit algebraic power iterations, the proposed training procedure is based on a series of alternate (ping-pong) transmissions between the two devices over a reciprocal channel. During the transmissions, the devices update their digital beamformers by conjugation and normalization operations on the received digital signal, while the analog beamformers are progressively updated by a simple “beam split and drop” strategy that tracks the directions from which signals with largest magnitude are being received. The resulting scheme has minimal computational complexity and converges with only a handful of iterations. As shown in the numerical assessment, the method approximates the top singular mode of the channel, hence performing very closely to optimal beamforming.

A source traffic model for machine-to-machine communications is presented in this paper. We consider a model in which devices operate in a regular mode until they are triggered into an alarm mode by an alarm event. The positions of devices and events are modeled by means of Poisson point processes, where the generated traffic by a given device depends on its position and event positions. We first consider the case where devices and events are static and devices generate traffic according to a Bernoulli process, where we derive the total rate from the devices at the base station. We then extend the model by defining a two-state Markov chain for each device, which allows for devices to stay in alarm mode for a geometrically distributed holding time. The temporal characteristics of this model are analyzed via the autocovariance function, where the effect of event density and mean holding time are shown.

Propagation channels with maximum excess delay exceeding the duration of the cyclic prefix (CP) in OFDM systems cause intercarrier and intersymbol interference which, unless accounted for, degrade the receiver performance. Using tools from Bayesian inference and sparse signal reconstruction, we derive an iterative algorithm that estimates an approximate representation of the channel impulse response and the noise variance, estimates and cancels the intrinsic interference and decodes the data over a block of symbols. Simulation results show that the receiver employing our algorithm outperforms receivers applying traditional interference cancellation and pilot-based schemes, and it approaches the performance of an ideal receiver with perfect channel state information and perfect interference cancellation capabilities. We highlight the relevance of our algorithm in the context of both current and future wireless communications systems. By enabling the OFDM receiver experiencing these harsh conditions to locally cancel the interference, our design circumvents the spectral efficiency loss incurred by extending the CP duration, otherwise a straightforward solution. Furthermore, it sets the premises for the development of receivers for multicarrier systems like 5G, in which the subcarrier orthogonality may be relaxed or the frame duration shortened, at the expense of cutting down the CP or even removing it altogether.

This letter deals with turbo equalization for coded data transmission over intersymbol interference (ISI) channels. We propose a message-passing algorithm that uses the expectation propagation rule to convert messages passed from the demodulator and decoder to the equalizer and computes messages returned by the equalizer by using a partial Gaussian approximation (PGA). We exploit the specific structure of the ISI channel model to compute the latter messages from the beliefs obtained using a Kalman smoother/equalizer. Doing so leads to a significant complexity reduction compared to the initial PGA implementation. Results from Monte Carlo simulations show that the proposed approach leads to a significant performance improvement compared to state-of-the-art turbo equalizers and allows for trading performance with complexity.

In this letter, a message-passing algorithm that combines belief propagation and expectation propagation is applied to design an iterative receiver for intersymbol interference channels. We detail the derivation of the messages passed along the nodes of a vector-form factor graph representing the underlying probabilistic model. We also present a simple but efficient method to cope with the “negative variance” problem of expectation propagation. Simulation results show that the proposed algorithm outperforms, in terms of bit-error-rate and convergence rate, a LMMSE turbo-equalizer based on Gaussian message passing with the same order of computational complexity.

In this letter, we design iterative receiver algorithms for joint frequency-domain equalization and decoding in a single carrier system assuming perfect channel state information. Based on an approximate inference framework that combines belief propagation (BP) and the mean field (MF) approximation, we propose two receiver algorithms with, respectively, parallel and sequential message-passing schedules in the MF part. A recently proposed receiver based on generalized approximate message passing (GAMP) is used as a benchmarking reference. The simulation results show that the BP-MF receiver with sequential passing of messages achieves the best BER performance at the expense of higher computational complexity compared to that of the GAMP receiver. The parallel BP-MF receiver has complexity similar to that of GAMP, but its low convergence rate yields poor performance, especially under high signal-to-noise ratio conditions.

In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of multipath components' gains with a hierarchical representation of the Bessel K probability density function; a highly efficient, fast iterative Bayesian inference method is then applied to the proposed model. The resulting estimator outperforms other state-of-the-art Bayesian and non-Bayesian estimators, either by yielding lower mean squared estimation error or by attaining the same accuracy with improved convergence rate, as shown in our numerical evaluation.

Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the ℓ1-norm of the parameter of interest. However, other penalization terms have proven to have strong sparsity-inducing properties. In this work, we design pilot-assisted channel estimators for OFDM wireless receivers within the framework of sparse Bayesian learning by defining hierarchical Bayesian prior models that lead to sparsity-inducing penalization terms. The estimators result as an application of the variational message-passing algorithm on the factor graph representing the signal model extended with the hierarchical prior models. Numerical results demonstrate the superior performance of our channel estimators as compared to traditional and state-of-the-art sparse methods.

We design iterative receiver schemes for a generic communication system by treating channel estimation and information decoding as an inference problem in graphical models. We introduce a recently proposed inference framework that combines belief propagation (BP) and the mean field (MF) approximation and includes these algorithms as special cases. We also show that the expectation propagation and expectation maximization (EM) algorithms can be embedded in the BP-MF framework with slight modifications. By applying the considered inference algorithms to our probabilistic model, we derive four different message-passing receiver schemes. Our numerical evaluation in a wireless scenario demonstrates that the receiver based on the BP-MF framework and its variant based on BP-EM yield the best compromise between performance, computational complexity and numerical stability among all candidate algorithms.

In this contribution, a multi-user receiver for M-QAM MIMO-OFDM operating in time-varying and frequency-selective channels is derived. The proposed architecture jointly performs semi-blind estimation of the channel weights and noise inverse variance, serial interference cancellation and decoding in an iterative manner. The scheme relies on a variational message-passing approach, which enables a joint design of all these functionalities or blocks but the last one. Decoding is performed using the sum-product algorithm. This is in contrast to nowadays proposed approaches in which all these blocks are designed and optimized individually. Simulation results show that the proposed receiver outperforms in coded bit-error-rate a state-of-the- art iterative receiver of same complexity, in which all blocks are designed independently. Joint block design and, as a result, the fact that the uncertainty in the channel estimation is accounted for in the proposed receiver explain this better performance.

In this paper we present a refined model of the wireless multipath channel along with a thorough analysis on the impact of spatial smoothing techniques when used for improved channel estimation. The state-of-the-art channel estimation algorithm for pilot-aided OFDM systems is robustly designed and operates without knowledge of the time-varying multipath propagation delays in the wireless channel. However, algorithms exploiting knowledge of these time-varying delay parameters can outperform the state-of-the-art solution. We demonstrate from simulations how the Unitary ESPRIT algorithm together with spatial smoothing techniques exhibit a promising potential for multipath propagation delay estimation. Furthermore, we show that the optimum smoothing parameters depend notably on the channel model assumed, specifically in terms of the dynamical behavior of the multipath delays.

In this paper, we present a novel iterative receiver for MIMO-OFDM systems with synchronous interferers. The receiver is derived based on the Kullback-Leibler divergence minimization framework, and combines channel estimation, interference cancellation and residual noise estimation in an iterative manner. By using both the pilot and data symbols, the channel estimator improves the accuracy of the estimates in each iteration, which leads to a more effective interference cancellation and data detection process. A performance evaluation based on Monte-Carlo simulations shows that the proposed scheme can effectively mitigate the effect of interferers, and operates very close to the single-user performance even in severe interference scenarios.

In this paper, we present a novel approach for pilot-aided channel estimation in OFDM systems with synchronous co-channel interference. The estimator is derived based on the Kullback-Leibler divergence minimization framework. The obtained solution iteratively updates both the desired user's and the interferer's channels, using a combination of linear minimum mean squared-error (LMMSE) filtering and interference cancellation, avoiding the complex matrix inversions involved in the full LMMSE channel estimation approach. Estimation of the noise variance is also included in the iterative algorithm, accounting for Gaussian noise and residual interference after each iteration. The estimates of both channels are used at the equalizer to reject the interfering signal, thus mitigating the degradation due to co-channel interference. Simulation results show that the receiver using the proposed estimator performs as good as the one employing the full LMMSE estimator and very closely to a receiver having perfect knowledge of the channel coefficients.

The paper deals with turbo detection techniques for Single User Multiple-Input-Multiple-Output (SU MIMO) antenna schemes. The context is on the uplink of the upcoming Long Term Evolution - Advanced (LTE-A) systems. Iterative approaches based on Parallel Interference Cancellation (PIC) and Successive Interference Cancellation (SIC) are investigated, and a low-complexity solution allowing to combine interstream interference cancellation and noise enhancement reduction is proposed. Performance is evaluated for Orthogonal Frequency Division Multiplexing (OFDM) and Single Carrier Frequency Division Multiplexing (SC-FDM) as candidate uplink modulation schemes for LTE-A. Simulation results show that, in a 2times2 antenna configuration, the turbo processing allows a consistent improvement of the link performance, being SC-FDM the one having higher relative gain with respect to linear detection. The turbo receiver's impact is however much reduced for both modulation schemes in a 2times4 configuration, due to the higher diversity gain provided by the additional receive antennas.

In this paper, we investigate different multiple-input multiple-output (MIMO) receiver structures based on MMSE filtering and sequential interference cancellation (SIC) for the downlink of the 3GPP long term evolution (LTE) system. We divide them into two approaches: symbol-SIC receivers, in which the detection and interference cancellation is done independently for each subcarrier, and codeword-SIC structures, in which the processing is carried out for each independently-coded stream by including the turbo-decoder inside the feedback loop. The results show that symbol-SIC receivers need to take into account the propagation of errors in the interference cancellation to provide the turbo decoder with reliable soft bit values. However, these are clearly outperformed by codeword-SIC schemes, due to the error correction capabilities of the turbo-decoder inside the feedback loop. We show that the best tradeoff between computational complexity and receiver performance is achieved by only cancelling the interference of a codeword when this has been successfully decoded.

In this paper, an iterative enhancement of the robust Wiener filter (RWF) estimator is presented for a turbo-coded orthogonal frequency division multiplexing (OFDM) system under the umbrella of the 3GPP long term evolution. The proposed scheme can operate with uncoded or coded feedback, and outperforms the conventional linear RWF in the whole signal-to-noise ratio (SNR) range with both approaches. Results show that most of the gain is obtained in the first iteration of the algorithm, and better performance is achieved with the coded feedback scheme. A good tradeoff between accuracy and complexity is achieved by selecting a low number of turbo coding iterations (TCI) in the iterative loop and concentrating most of them at the final decoding stage. Following this design, cell spectral efficiency gains of around 2.7% and 6.5% can be obtained with respect to linear RWF for micro- and macro-cell scenarios respectively.

This paper studies common linear frequency direction pilot- symbol aided channel estimation algorithms for orthogonal frequency division multiplexing in a UTRA long term evolution context. Three deterministic algorithms are analyzed: the maximum likelihood (ML) approach, the noise reduction algorithm (NRA) and the robust Wiener (RW) filter. A closed form mean squared error is provided for these three algorithms. Analytical and simulation results show that, in the presence of virtual subcarriers, the ML can suffer large performance degradation due to ill-conditioned matrix issues. A solution to this problem is to use the Tikhonov regularization method giving the NRA. The equivalence between the NRA and the RW is proved analytically. A practical implementation of the NRA and RW is proposed based on partial-input partial-output FFT, leading to 6 to 8 times lower complexity than the reference implementation.

In this paper, the effects of phase noise on the spectral efficiency of the next generation of OFDM based mobile systems with channel estimation is investigated. The simulation context and parameter settings are taken from the 3GPP Evolved UTRA (E-UTRA) study item, focusing on an OFDM downlink single antenna system in 20 MHz bandwidth. Phase noise is modeled as a Wiener-Levy process and several phase noise powers are evaluated. The OFDM coherent detection method is based on pilot assisted channel estimation (PACE) with Wiener based frequency domain interpolation and second order Gaussian interpolation for the time domain interpolation. The cell level spectral efficiency is also evaluated for micro and macro-cell scenarios. The simulation results indicate that the phase noise effect in E-UTRA downlink can be reduced by using high performance local oscillator or by placing pilots in every OFDM symbols