In this paper, partial update adaptive algorithms with a time-varying number of updating coefficients are proposed for channel identification and echo cancellation applications. The proposed algorithms present lower computational complexity than Partial Update NLMS M Maxima and a faster convergence than the Sparse Partial Update algorithm. The proposed algorithms allow a reduction of updates with hardly increasing the mean-square error in comparison to the benchmark algorithms. Sensitivity analysis to the proposed thresholds shows that a reduction of computational complexity may be reached with low MSE degradation.
Accurate channel state information in wideband multiple-input multiple-output (MIMO) systems is fundamentally constrained by pilot overhead, a challenge that intensifies as antenna counts and bandwidths scale toward 6G. This paper proposes a structure-informed hybrid estimator that formulates pilot-limited MIMO channel estimation as low-rank tensor completion from sparse pilot observations—a severely underdetermined inverse problem that prior tensor approaches avoid by assuming fully observed received signal tensors. Canonical polyadic (CP) and Tucker decompositions are comparatively analyzed: CP excels for specular channels whose rank-one multipath structure matches the CP parameterization exactly, while Tucker provides greater numerical stability at extreme pilot scarcity where CP exhibits heavy-tail divergence. A lightweight 3D U-Net learns residual components beyond the dominant low-rank structure, compensating for diffuse scattering and hardware non-idealities that algebraic priors alone cannot capture. On synthetic specular channels, Tucker completion achieves 10.88 dB NMSE improvement over least squares and 7.83 dB over orthogonal matching pursuit at ρ = 10% pilot density; CP outperforms Tucker by 13.11 dB at SNR=20 dB under the specular multipath model. On DeepMIMO ray-tracing channels, the hybrid estimator surpasses CP by 2.26 dB and Tucker by 4.80 dB at ρ = 8%, while remaining stable at ρ = 2% where CP diverges; algebraic structure consistently outperforms unconstrained deep learning across the full pilot-density range, with a margin growing from 1.53 dB at ρ = 2% to 5.67 dB at ρ = 20%. Empirical recovery threshold analysis confirms that sample complexity scales with intrinsic channel dimensionality—governed by the number of dominant propagation paths—rather than with the ambient tensor size
This paper presents a significant contribution to the field of 5G networking through the development and deployment of a Software-Defined Radio (SDR)-based 5G Stand-Alone (SA) network leveraging the Multi-Access Edge Computing (MEC) paradigm. Designed to deliver high-quality voice and video services using the Session Initiation Protocol (SIP) Kamailio Server, our implementation significantly reduces latency by integrating Virtualized Network Functions (VNFs) and Docker containerized elements onto a single workstation, enhancing the efficiency of 5G Core operations. Built entirely on open-source projects, the proposed mobile network successfully provides both Voice-overNew Radio (VoNR) and Video-over-New Radio (ViNR) services using SDR. Notably, this work introduces the first publicly available ViNR prototype for a comprehensive 5G SDR solution, thereby filling a gap in existing research and development. We further detail a complete end-to-end call flow for both VoNR and ViNR, utilizing SIP Kamailio, and provide a valuable resource for researchers and developers. The performance of the proposed network is evaluated through Quality of Service (QoS) metrics, and the computational resource allocation is analyzed. We utilize two smartphones as representatives of typical mobile users to simulate their behavior. The results are then comprehensively compared with those obtained from a high-tech radio communication test station platform, a platform commonly used for certifying terminals, chipsets, and devices against 3GPP standards. Our findings demonstrate that the proposed SDR MEC network exhibits performance closely aligned with that of the robust, established test platform, validating its effectiveness in controlled environments. This research significantly advances the state-of-the-art in 5G SDR MEC networks by providing scalable, efficient, and cost-effective solution for delivering IP multimedia services, with a particular focus on enabling and optimizing Video-over-New Radio (ViNR).
The adoption of wireless technologies in industrial environments has been a highly relevant topic of investigation, considering both the interplay between automation and industrial data communication systems as well as the goal to meet the stringent communication requirements of Industry 4.0, where network reliability and resilience are critical considerations. In such Integrated Manufacturing Systems (IMS), where people and machines interact in an industrial environment, safety aspects also play a fundamental role. In this context, this work investigates a new application framework for 5G URLLC communications in Industry 4.0, taking advantage of the low latency provided by the 5G infrastructure, through the integration of automation safety systems into an IMS architecture. In our setup, an Industrial Internet of Things, IIoT, device uses 5G connectivity to send an emergency stop command to an Industrial Autonomous Mobile Robot. Our experiment achieved a maximum latency below 1 ms with a block error rate (BLER) below 10^-5 on the air interface, including scenarios of high consumption of Physical Resource Blocks (PRBs). The results reinforce the great potential for using 5G networks for worker safety systems and automation elements in industrial environments.
Video conferencing has become a widely adopted solution for meetings involving remote participants and rapid communication. This service must comply with the required Quality of Service (QoS) parameters to meet user expectations. The primary purpose of this work is to assess the energy consumption and jitter of a real-time testbed for Video over LTE (ViLTE) and Voice over LTE (VoLTE) over an IP Multimedia Subsystem (IMS). Modern technology is deployed to recreate the best feasible scenario for this goal. Based on the results of our test experiments, VoLTE can drain the battery faster than ViLTE, which consumes up to 112% of the battery while using the LTE B7 band. Our study provides an analysis of jitter evaluation for video conversations, evaluates power usage for ViLTE and VoLTE services, and quantifies jitter values.
