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 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.

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.

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 an app agnostic QoE deduction method that can be used for on-line traffic prioritization. Our implemented method extracts QoE information directly from the screen of mobile devices and this information is utilized for video traffic prioritization to improve user QoE. We evaluate our method and show that it can realize 80-100% of the achievable gain of the state-of-the-art methods with 50% less traffic that needs to be prioritized to achieve this. We provide deployment alternatives for traffic prioritization and evaluate performance impact on achievable gain.

In this paper we propose evaluation models and calculate range of potential gains for enhancing the average web-browsing QoE of all the users in a mobile network with given capacity. We investigate a method that takes capacity from users during QoE insensitive periods. The gained capacity is distributed among users being in QoE affecting state. Our models of web page network prioritization show that the potential gain for QoE affecting periods highly depends on the correlation between data downloaded during QoE affecting and QoE insensitive periods. The numerical evaluation of wide range of scenarios with web traffic shows that the achievable gain is up to 104% i.e., halving the download time of QoE affecting part. Network prioritization of web pages has good potential to improve web-browsing QoE.

As the all-IP architecture for W-CDMA based 3rd generation cellular networks matures within the ITU and 3GPP, there is a growing interest in devising resource reservation schemes that allocate both radio and IP resources in the access part of the network. We consider both the control- and the user plane of an end-to-end scenario with both IP and radio level QoS mechanisms and propose the addition of new parameters to the RSVP/IntServ model. Simulations indicate that these new parameters improve the efficiency of the radio resource allocation.

While several papers and standards promote ATM in combination with the ATM Adaptation Layer Type 2 (AAL2) as the basic switching and multiplexing technology for 3/sup rd/ generation mobile access networks, very little work addresses the issue of AAL2 call admission control (CAC). The hardship of AAL2 CAC comes from the fact that AAL2, unlike ATM, supports variable packet size traffic. In addition, when applied in the cellular environment, the AAL2 CAC needs to co-operate with the radio interface (RI) resource management. In this paper we develop and compare AAL2 CAC algorithms that operate on the AAL2 traffic parameters currently considered by the ITU-T. Based on this comparison we find that one of these algorithms, employing the well-known Hoeffding inequality provides a reasonable trade-off between complexity and precision.