One of the main challenges in integrated sensing and communications (ISAC) are interference signals caused by the communication user equipment (UE) emitting in the sensing area. It is therefore necessary to design appropriate transmitter and receive precoding schemes allowing to mitigate interference signals caused by the UEs. Subspace-based algorithms, such as the multiple signal classification (MUSIC) algorithm, are widely used in radar sensing, and are promising candidates for parameter estimation in evolving integrated sensing and communications (ISAC) networks. The MUSIC algorithm gives a consistent estimate of the angle-of-arrival (AoA) localization function when the number of time samples is large as compared to the number of receive antennas. However, when the number of time samples is of the same order of magnitude as the number of receiver antennas, the traditional MUSIC algorithm may fail to separate closely spaced targets. In this paper we propose an improved version of the MUSIC algorithm, that is particularly tailored for ISAC, where bistatic sensing is performed by multi-antenna cellular base stations for simultaneous AoA estimation of sensing targets and communication UEs. As the simulation results show, the proposed method achieves superior performances compared to the traditional MUSIC algorithm, especially when the number of available time samples is of the same size or smaller as compared to the number of receiver antennas. Moreover, the proposed method allows to separate targets and UEs located close to each other, even when the traditional MUSIC fails.

The wireless communication systems of today rely to a large extent on the condition of the accessible channel state information (CSI) at the transmitter and receiver. Channel aging, denoting the temporal and spatial evolution of wireless communication channels, is influenced by obstructions, interference, traffic load, and user mobility. Accurate CSI estimation and prediction empower the network to proactively counteract performance degradation resulting from channel dynamics, such as channel aging, by employing network management strategies such as power allocation. Prior studies have introduced approaches aimed at preserving high-quality CSI such as temporal prediction schemes, particularly in scenarios involving high mobility and channel aging. Conventional model-based estimators and predictors have historically been considered state-of-the-art. Recently, the development of artificial intelligence (AI) has increased the interest in developing models based on AI. Previous works have shown high potential of AI-aided channel estimation and prediction, which inclines the state-of-the-art title from model-based methods to be confiscated. However, there are many aspects to consider in channel estimation and prediction employed by AI in terms of prediction quality, training complexity, and practical feasibility. To investigate these aspects, this chapter provides an overview of state-of-the-art neural networks, applicable to channel estimation and channel prediction. The principal neural networks from the overview of channel prediction are empirically compared in terms of prediction quality. An innovative comparative analysis is conducted for five prospective neural networks characterized by distinct prediction horizons. The widely acknowledged tapped delay line (TDL) channel model, as endorsed by the Third Generation Partnership Project (3GPP), is employed to ensure a standardized evaluation of the neural networks. This comparative assessment enables a comprehensive examination of the merits and demerits inherent in each neural network. Subsequent to this analysis, insights are offered to provide guidelines for the selection of the most appropriate neural network in channel prediction applications.

This paper considers wireless communication assisted by a reconfigurable intelligent surface (RIS), focusing on the two-timescale approach, in which the RIS phase shifts are optimized based on channel statistics to mitigate the overheads associated with channel estimation. It is shown that, while the power captured by the RIS scales linearly with the number of its elements, the two-timescale beamforming gain upon re-radiation towards the receiver saturates rapidly as the number of RIS elements increases, for a broad class of power angular spectra (PAS). The ultimate achievable gain is determined by the decay rate of the PAS in the angular domain, which directly influences how rapidly spatial correlations between RIS elements diminish. The implications of this saturation on the effectiveness of RIS-assisted communications are discussed.

Previous works have investigated fundamental trade-offs in bistatic integrated sensing and communication (ISAC) systems, where the trade-offs are due to sharing the transmit resources between the sensing and communication signals. Interestingly, the ISAC trade-offs due to using an integrated multi-antenna receiver - where the communication and sensing signals cause interference to one another - are seldom studied. In this paper we study three bistatic ISAC structures, in which either the transmitter and/or the receiver serve as ISAC entities and study the transmit and receiverside trade-offs and the achievable sensing and communication performance. In the fully integrated scenario, both the transmitters of the sensing and communication signals and the receiver of the two signals are integrated, which serves as a benchmark for the cases in which either the transmitters or the receivers of the sensing and communication signals are separated. Specifically, we derive the classical and the Bayesian Cramé-Rao bounds, which indicate that relaxing the transmitter and/or receiver-side trade-offs benefits both the sensing and communications performance at the expense of using more hardware, antenna, and transmit power resources. These analytical and numerical results can serve as a foundation for designing the architecture for bistatic ISAC networks. Based on these insights, we discuss some open questions that require further research.

To meet the growing demands for connectivity and reliability in cellular networks, it is essential to ensure reliable quality of service (QoS) guarantees for end users. The integration of predictive QoS (pQoS) in cellular networks enables proactive fulfillment of QoS requirements for a diverse range of applications, including intelligent transportation systems. This study presents a pQoS framework in cellular networks, particularly for connected vehicles, that divides the road into segments, clusters them, and assigns a pQoS model to each cluster. By implementing this framework, we mitigate the concept drift of the pQoS model induced by variations in the propagation environment and interference. Each predictive cluster model is locally trained on vehicles traveling within the cluster boundaries using federated learning. A significant challenge is balancing the trade-off between the number of clusters, prediction accuracy, and communication overhead for updating local models. This trade-off suggests the novel problem of performing a joint optimization of the training and number of clusters. To address such difficult optimization, we propose an iterative approximate solution using proximal alternative minimization for which we provide convergence guarantees. Ultimately, by evaluations with real-world data, our numerical findings reveal that our proposed clustered predictive model reduces the mean absolute percentage error by 8%, and the mean absolute error by 7%, compared to conventional predictive approaches proposed by prior studies.

Distributed multiple-input and multiple-output (D-MIMO) technology is a promising candidate to be integrated in beyond fifth generation networks offering uniform quality of service along the network coverage and higher overall system capacity. It leverages the cooperation of many antenna arrays spread over the coverage area that jointly and coherently serve user equipment devices. While the spectrum efficiency benefits of D-MIMO (sometimes also referred to as cell-free technology) are well documented, the corresponding energy efficiency (EE) of such networks has received more attention in recent years as concerns about sustainability become central in future 6G systems. In this context, this paper proposes and investigates the combined effect of intelligent access point clustering and power control to enhance D-MIMO’s EE. While many works on D-MIMO assume that the whole network serves every user, we consider scalability and massive MIMO constraints to address the practical issues of a fully connected network in terms of high computational complexity, elevated signaling bandwidth, and limitations of MIMO’s spatial degrees of freedom. To this end, we propose a resource-aware graph-based clustering method combined with a deep-learning-based power control. Comprehensive computer simulations demonstrate that the proposed strategy significantly enhances the network’s EE, while producing comparable spectral efficiency performance to a fully connected scenario, while also outperforming a state-of-the-art existing clustering approach.

Several previous works have pointed out that the channel aging effect may severely degrade the performance of wireless systems. This effect may be more pronounced in cell-free networks due to two main reasons. First, designing centralized precoders for cell-free (CF) systems requires the exchange of a large amount of channel state information (CSI) data between access points and a central processing unit, which may cause considerable processing delays. Secondly, the CF architecture requires keeping track of multiple aging channels, since user equipment devices are served by multiple cell sites. In this paper, we consider a CF multiple input multiple output (MIMO) system that uses joint coherence transmission and operates in the presence of channel propagation clustering under high Doppler frequencies. Unlike previous works, which focus on analyzing the impact of channel aging, this paper proposes a Kalman filter-based directional CSI prediction scheme that relies on a set of underlying $P$-th order autoregressive processes that correspond to the active links of the CF system. The extended Kalman filter is chosen with the purpose of predicting the channel path parameters. Using the predicted directional CSI, we design centralized hybrid precoding. Our results indicate that the proposed CSI prediction and precoding schemes outperform related benchmarks schemes and a 3rd order AR process model is a good engineering trade-off between model complexity and CSI prediction performance in practically relevant CF scenarios.

We propose a novel pilot-free multi-user uplink framework for integrated sensing and communication (ISAC) in mm-wave networks, where single-antenna users transmit orthogonal frequency division multiplexing signals without dedicated pilots. The base station exploits the spatial and velocity diversities of users to simultaneously decode messages and detect targets, transforming user transmissions into a powerful sensing tool. Each user’s signal, structured by a known codebook, propagates through a sparse multi-path channel with shared moving targets and user-specific scatterers. Notably, common targets induce distinct delay–Doppler–angle signatures, while stationary scatterers cluster in parameter space. We formulate the joint multi-path parameter estimation and data decoding as a 3D super-resolution problem, extracting delays, Doppler shifts, and angles-of-arrival via atomic norm minimization, efficiently solved using semidefinite programming. A core innovation is multi-user fusion, where diverse user observations are collaboratively combined to enhance sensing and decoding. This approach improves robustness and integrates multi-user perspectives into a unified estimation framework, enabling high-resolution sensing and reliable communication. Numerical results show that the proposed framework significantly enhances both target estimation and communication performance, highlighting its potential for next-generation ISAC systems.

In this letter, we address parametric channel estimation in a multi-user multiple-input multiple-output system within the radiative near-field of the base station array with aperture antennas. We investigate a two-dimensional multiple signal classification algorithm (2D-MUSIC) to estimate both the range and the azimuth angles of arrival for the users' channels, utilizing parametric radiative near-field channel models. We analyze the performance of the algorithm by deriving the Cram & eacute;r-Rao bound (CRB) for parametric estimation, and its effectiveness is compared against the least squares estimator, which is a non-parametric estimator. Numerical results indicate that the 2D-MUSIC algorithm outperforms the least squares estimator. Furthermore, the results demonstrate that the performance of 2D-MUSIC achieves the parametric channel estimation CRB, which shows that the algorithm is asymptotically consistent.

The optimization of the pilot-to-data power ratio (PDPR) is a recourse that helps wireless systems to acquire channel state information while minimizing the pilot overhead. While the optimization of the PDPR in cellular networks has been studied extensively, the effect of the PDPR in reconfigurable intelligence surface (RIS)-assisted networks has hardly been examined. This letter tackles this optimization when the communication is assisted by a RIS whose phase shifts are adjusted on the basis of the statistics of the channels. For a setting representative of a macrocellular deployment, the benefits of optimizing the PDPR are seen to be significant over a broad range of operating conditions. These benefits, demonstrated through the ergodic minimum mean squared error, for which a closed-form solution is derived, become more pronounced as the number of RIS elements and/or the channel coherence grow large.