The RAN (Radio Access Network) has undergone significant changes to meet the diverse requirements of various use cases. These changes include rewriting the network architecture, protocols, and algorithms to optimize performance and efficiency. The Radio Link Control (RLC) layer offers Automatic Repeat Request (ARQ) for error recovery and reliable packet delivery in Acknowledgement Mode (AM). In AM mode, the RLC receiver uses a t-Reassembly timer to allow time for recovery and reordering at lower layers. Additionally, a t-StatusProhibit timer is employed to prevent excessive transmission of Status Reports and to allow sufficient time for the MAC layer to recover errors through HARQ retransmissions and to update the ACK/NACK status to the transmitter side. In the AM Mode, data packets at RLC transmitter are only freed after receiving an acknowledgment of successful reception through a Status Report from the RLC receiver. In low-memory systems, delays in transmitting the Status Report caused by a high t-StatusProhibit timer value can lead to significant consumption of packet buffer memory. This paper introduces a solution that focuses on minimizing the packet buffer memory requirement at the transmitter RLC, facilitating the design of cost-effective and memory-efficient devices. Experimental results demonstrate that the proposed solution decreases the transmitter RLC packet data buffer memory requirement by approximately 50%, with one additional status report within a t-StatusProhibit timer interval without affecting the RLC protocol's functional requirements.

With 5G a reality, communication is not only getting faster and better, but also the new verticals, which were so far not serviced by the cellular industry, are supported. One of the areas where 5 G has promising impact is enabling the Internet of Things (IoT) communication. With higher spectrum and bandwidth, 5G enables massive number of devices to connect to the IoT networks. At the same time, the exponential growth of the number of connected devices puts unprecedented load on cellular infrastructure. Further, 5G enables IoT applications to control more devices remotely where low latency and high throughput are critical performance criteria - like healthcare and factory applications. For such applications, it is essential that the application service remains available and be responsive always. Continuous monitoring at all the layers is critical in ensuring the seamless and uninterrupted service availability, thus improving the user experience and efficient use of resources. In this paper, the authors explain the benefits of Application Service Monitoring (ASM) in coordination with the underlying transport in providing the best quality of experience for the user through a framework using 3GPP Service Enabler Architecture Layer (SEAL). The paper also provides a mathematical model of the Quality of Service (QoS) adaptation through Network and Vertical Application Layer (VAL) coordination feature of framework and substantiate the same with the simulation and its results. The paper finally provides simulation results explaining the benefits of using ASM framework with SEAL.

Millimeter-wave (mmWave) technology is integral to sixth-generation (6G) communication networks due to its high data rates and bandwidth. However, signal degradation in Non-Line-of-Sight (NLoS) regions, particularly in urban environments, presents significant challenges. This paper proposes a map-assisted approach for optimal placement of wall-mounted mmWave base stations (wBSs) to extend coverage in shadowed NLoS areas. Using computational geometry and ray-tracing, we identify NLoS regions and formulate a low-complexity set-cover algorithm to determine minimal wBS deployment. Our results demonstrate improved coverage and signal strength in NLoS regions, validated by ray-tracing simulations. This method supports cost-effective planning for 6G mmWave networks.

Industrial processes face many critical challenges such as constrained communication, high costs of operation and traceability in real-time. Such opportunities do represent industries like manufacturing, offshore operations, and even space exploration. This paper addresses the challenge of implementing cost-effective and scalable digital twin frameworks for remote industrial process supervision, focusing on constrained communication and traceability. These challenges are critical in sectors like manufacturing, offshore operations, and space exploration, where real-time monitoring and predictive maintenance are essential for operational efficiency. A hybrid method combining modeling, simulation, and hardware experimentation is used in creating a digital twin of a direct current (DC) motor by the amazon wave service (AWS) services, such as Internet of things (IoT) SiteWise, Greengrass, and TwinMaker. With this framework, data transmission overhead was reduced by 30% and anomaly detection accuracy improved by 20% compared to traditional methods. It demonstrated real-time visualization, anomaly detection, and rule-based monitoring through Grafana dashboards. This work benefits industries by implementing a scalable and adaptable way of digital twin implementation towards predictive maintenance, operational optimization, and cost-efficient solutions for remote monitoring.

Accurate channel estimation is essential for reliable downlink synchronization (DLSync) in 5G and beyond wireless systems, especially during the initial access (IA) phase. This work focuses on synchronization signal blocks (SSBs), which play a crucial role in delivering system information from the base station (gNodeB) to user equipment (UE). We propose a deep learning-based approach using an inception-style neural network to estimate the channel across the SSB time-frequency grid. Our model outperforms traditional techniques such as demodulation reference signal (DMRS) interpolation and least squares (LS) estimation, especially under practical wireless conditions like multipath delay spread and Doppler shift. The proposed model achieves a bit error rate (BER) of 10−4at an SNR of 20 dB, significantly better than the 3 × 10−3BER of conventional methods. Moreover, we have observed 4-5 dB gain at high SNR with respect to LMMSE and LS estimators. These results demonstrate that our model offers more reliable and energy-efficient synchronization, even in challenging real-world environments.

Radio Access Network (RAN) data plane systems in 5G are expected to support multiple user traffic flows demanding high throughput and low latency. With increased usage of available services and applications, a single user will tend to have multiple streams of Internet Protocol (IP) flow and applications supported over a single Protocol Data Unit (PDU) session. Applications of similar Quality of Service (QoS) requirements get mapped to a single Data Radio Bearer (DRB) over the RAN. Several studies have found that over 90 % of total Internet traffic is Transmission Control Protocol (TCP) traffic. Single DRB can have multiple TCP flows in it. New Radio (NR) Packet Data Convergence Protocol (PDCP) layer delivers packets in-order to the upper layers for the configured DRB. When out-of-order packets are received, the reordering timer is triggered at PDCP and packets will wait in the reordering window until missing packets are received or the reorder timer expires. So a packet loss in one flow in the DRB can affect all the flows in the DRB and can lead to increased latency and reduced throughput in other TCP flows without loss. We propose a novel method to segregate TCP flows at a DRB in PDCP and handle data loss specific to each flow, so that flows without data loss are not impacted. When tested with multiple TCP flows under diverse loss conditions, the proposed solution yields a 4–9 % reduction in average latency and round-trip time (RTT) while boosting throughput when compared to NR PDCP.

Massive multiple-input multiple-output (mMIMO) and millimeter waves (mmWaves) are considered to be key technologies for 5G and beyond wireless communications. Massive-MIMO at mmWave frequencies is coupled with advanced beamforming algorithms, to meet the high data rates and stringent latency requirements. The sensitivity of mmWaves to physical obstacles causes significant attenuation of signal leading to link failure between the user and base station (BS). Hence, uninterrupted connectivity to a user can be established via proactive handovers (HOs) between BSs using deep-learning (DL) models for future blockage and beam prediction. In this paper, we present a data-driven neural network approach using Convolution neural networks (CNN) and recurrent neural network (RNN) models for the prediction of future blockages and beams to enable proactive HO and to facilitate seamless connectivity in 5G and beyond ultra-dense networks (UDNs). We validated our proposed deep-learning models’ efficacy for blockage and beam prediction. The evaluation results of CNN and RNN models demonstrate an accuracy of 99% for blockage predictions and more than 90% in the case of beam predictions, with low computational complexity. The proposed DL models have the potential for commercial deployments in next-generation radio access networks (RAN) systems like O-RAN, vRAN, and C-RAN.

We consider a multicarrier (MC) single-cell mobile communication system with In-band full duplex (IBFD) capable base station (BS) and half duplex (HD) mobile nodes. The BS employs non-orthogonal multiple access (NOMA) technique for both uplink (UL) and downlink (DL) communication. We assume superposition of more than two users can be done in any given direction using NOMA. We maximize sum rate subject to total power constraints at the BS and at the UL users. We propose a modification to the particle swarm optimization (PSO) algorithm to reduce computational complexity. We compare the performance of our modified PSO with the block coordinated descent (BCD) algorithm proposed in baseline scheme. Our simulation results show significant improvement in terms of sum rate when compared to baseline algorithm. We also compare the performance with half duplex BS employing NOMA in a given direction.

One of the key technologies of next-generation 6G networks is millimeter-wave communications that will deploy a large number of antennas at the base station enabling narrow beams toward user locations to mitigate the path loss. Conventional methods have resulted in high training overhead in finding the best beam pair to obtain beam alignment between the base station and a user. This paper proposes a data-driven neural network approach to intelligently perform the beam selection between the transmitter-receiver pair. We propose a convolution neural network (CNN) based beam selection method trained from simulator-generated beam dataset. We use skip connections and hyperparameter optimization to balance the trade-off in accuracy and computational complexity. We validate the efficacy of our proposed method by comparing it with other conventional and machine learning-based approaches. Evaluation results show higher accuracy (> 70%) while reducing the computational complexity upto 15%.

Determining the best locations for Intelligent Reflecting Surface (IRS) is crucial in attaining an intelligent and controllable wireless environment, where increased probability of Line-of-Sight (LoS) links results in achieving higher performance gains. This paper investigates the potential in improving the performance of IRS-assisted wireless communication systems for Non-Line-of-Sight (NLoS) users. We particularly evaluate the coverage performance of placing multiple IRSs within an area map that consists of pre-installed Base Stations (BSs). We propose a fast and novel Angle-Direction Localization algorithm to determine optimal IRS locations such that a virtual LoS (VLoS) path to the NLoS users can be established. Through our proposed algorithm, we show an increase in the LoS connections to the NLoS users in the area map using optimally localized IRSs. Lastly, we show minimized end-to-end path loss levels at the NLoS user locations on the placement of IRSs using radio maps. Our results showcase that optimally localized IRSs, play an important role in extending the coverage of an existing network.

Accurate channel estimation in the millimeter-wave (mmWave) based wireless communication systems is challenging and involves a lot of computational costs. The mmWave frequency band has its advantages and disadvantages. At higher frequency mmWave bands, due to smaller wavelengths, we can pack a large number of antennas compared to lower frequency bands. However, the main disadvantages of the mmWave system are computing accurate channel estimation, smaller coverage, and high signal absorption. Besides, when multiple-input multiple-output (MIMO) systems operated over mmWave frequencies, it makes the channel estimation even more intricate in terms of computational complexity and estimation accuracy. In this paper, we plan to address these limitations and improve channel accuracy; we proposed a Continual Learning (CL)-based method for channel estimation in mmWave MIMO systems. Besides, we also proposed an activation function that is numerically stable and robust against early saturation. We discussed several channel estimation algorithms from the literature, also evaluated and compared their performances via numerical simulations. Our simulation results show that the proposed CL-based method outperforms the existing minimum mean squared error (MMSE)-based channel estimators in terms of precision. Furthermore, based on our experiments, we give insight into spectral efficiency with respect to the number of available channel observations.

This paper aims to classify signal and modulation classes of a given wireless signal with high accuracy using a model having a low number of parameters. We propose an end-to-end method to classify a wireless signal based on its signal and modulation type using a CNN-based architecture. The proposed architecture is similar to that of the LeNet-5. Firstly, we implement signal and modulation classification using a decision tree, followed by a random forest algorithm, classic examples of machine learning(ML) based algorithms. Since our dataset is a time series, we also implement using RNN-LSTM based model for the classification. The proposed model has fewer parameters than that of the CNN-based, RNN-LSTM based architectures. Moreover, it achieves better accuracy for a wide range of signal-to-noise ratios than a decision tree, random forest, RNN-LSTM based classifiers.

Owing to improved coverage and flexibility, the radio access network (RAN) functionalities are being virtualized in a sense that the base station will merely act as a radio unit, and all the baseband processing will occur in the cloud. Therefore, the baseband signal-processing algorithms need to be designed in a way that it can match the latency requirements. In this paper, we address one of the inherent but complex issues in baseband signal processing, namely, the log log-likelihood ratio (LLR) computation. In general, soft-decision rules are used for calculating the LLRs, which is computationally expensive. Thus, we attempt to exploit the benefits of hard-decision based LLRs for proposing a framework that uses soft decision only when the received symbols are closed to the decision boundary; otherwise, the framework uses hard decision. This helps us to keep the complexity low while meeting the desirable error performance. These schemes are suitable for incorporation in virtual RAN systems while considering appropriate QoS requirements.