Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario

The high throughput with low latency, massively connected devices, and effective utilization of spectrum for current wireless communication systems can be realized by adopting the fifth-generation (5G) new radio (NR) air interface. The key remarkable features that 5G NR presents are Ultra-Reliable Low-Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communications (mMTC). To meet thereof features, 5G NR exerts different multiple access and modulation techniques. This paper addresses the physical layer of 5G NR and more explicitly explores the transmission of 5G NR over the physical uplink shared channel (PUSCH) considering several parameters. For example, different sub-carrier spacings (SCSs) are taken into account for analyzing the performance of PUSCH in terms of throughput versus Signal-to-Noise Ratio (SNR). Moreover, the effect of the well-known modulation techniques such as Quadrature Phase Shift Keying (QPSK), different order of Quadrature Amplitude Modulation (QAM) (i.e., 16, 64, and 256) on throughput is studied. Later on, the number of base station (BS) and user equipment (UE) antennas are varied. Lastly, the performance of PUSCH over different propagation channel models (clustered delay line (CDL) and tap delay line (TDL)) is also investigated in this paper. The extensive simulation studies have proved that QPSK exhibits finer outcomes in low SNR regions while 256-QAM shows remarkable results in high SNR regions. The maximum throughput can be realized even in low SNR regime if the number of BS antennas is increased. In addition, high throughput value can be attained by increasing SCS.

With the prosperity of emerging applications, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6^{th}$ </tex-math></inline-formula> Generation mobile communication systems (6G) is coming at an unimaginable speed. It is expected to provide more intelligent, flexible, and secure services. As an essential pillar of 6G networks, ultra-Reliable Low Latency Communication (uRLLC) has promoted the vigorous development of intelligent communications. However, the existing networks cannot fully satisfy the strict and various requirements of uRLLC services, including delay, reliability and security. Considering the interaction between the physical layer and the upper layer, we propose a Cross-layer Flexible Security Solution (CFSS), which includes initiative waiting strategy, flexible transmission time interval scheduling strategy, and flexible pre-backup transmission strategy. While considering secure communication, CFSS could flexibly provide customized services to the users through cross-layer parameters configuration and resource allocation. In addition, we extend the Stochastic Network Calculus (SNC) modeling to the security field, and use Finite Blocklength Coding (FBC) to analyze the service process of uRLLC. Two cases of FBC are considered comprehensively, namely, given decoding error probability and given transmission rate. Finally, Experienced Meta-Asynchronous Advantage Actor-Critic (EM-A3C) algorithm is proposed to solve the complex optimization problem, the establishment of experience pool effectively improves the algorithm efficiency.

The theoretical and practical aspects of applying machine learning (ML) methods in the construction industry are considered, in which the increasing complexity of decision-making and the need to improve the efficiency of managing the lifecycle of capital construction projects drive the adoption of modern intelligent algorithms. The history of machine learning, its fundamentals, and existing ML methods arepresented. An analysis of the advantages and disadvantages of machine learning as a whole and of existing ML methods is conducted. A classification of machine learning methods is developed, tailored to the tasks addressed at various stages of the lifecycle of capital construction projects (engineering surveys, design, construction, operation, major repairs, reconstruction, and demolition). The conclusion highlights the high potential of applying machine learning in the construction industry to enhance prediction accuracy, optimize resource costs, and reduce risks at all stages of the lifecycle of capital construction projects.

With the continuous popularization and deepening of the Internet-of-Things (IoT) technologies, trillions of IoT Devices (IoTD) are connected to the network. The huge growth of wireless communication traffic and the surge of energy consumption make it a great challenge to support various requirements of IoTDs, such as ultrareliable and low latency. The 6th-generation (6G) network has put forward new goals and visions for green communication, network flexibility and intelligence, which are expected to solve these key challenges. In this article, we propose a cross-layer optimization scheme to achieve the trade-off between energy efficiency (EE) and spectral efficiency (SE) of the 6G enabled IoT networks, where the ultrareliable and low-latency applications are considered. Flexible self-organization of three parameters is realized, namely, transmission time interval (TTI), packet duplication (PD), and resource block (RB) allocation. The key technology of flexible TTI scheduling guarantees the reduction of latency, and the PD transmission can effectively improve the reliability. Furthermore, based on machine learning (ML) method, we propose the transfer asynchronous advantage actor–critic (TA3C) algorithm to realize parameter configuration and resource allocation. The simulation results show that the EE and SE tradeoff performance of our proposed flexible scheme is improved by at least 39.29% compared with the fixed parameter configuration. In addition, the TA3C algorithm has better convergence performance and reduces the algorithm complexity by up to 91.23% compared with other ML algorithms.

Orthogonal Frequency Division Multiplexing (OFDM) is now the de-facto modulation scheme of contemporary broadband wireless communication systems, such as 5G New Radio (NR), Wi-Fi 6/7, and emerging sixth-generation (6G) systems, because it has superior resistance to multipath fading, is a high spectral efficiency, and can support flexible resource allocation. Traditional OFDM receivers are highly dependent on pilot symbols or reference signals to estimate the channel, synchronize and equalize it. Yet, pilot insertion adds a large overhead in spectral, decreases the efficiency of the energy and constrains the system throughput, particularly in ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC) and high-mobility conditions. Such constraints have fueled the creation of pilotless OFDM reception techniques demand instinctive inferring of channel state information (CSI) at the receiver of received data without explicit pilot signals. The objective of implicit channel inference methods is to recover the response of the wireless channel based on statistical signal characteristics, data driven learning model, blind estimation applications, and sophisticated signal processing structures. These strategies minimize pilot overhead, enhance spectral efficiency, and support dynamic environment adaptive communication. The latest developments in machine learning, specifically deep neural networks, reinforcement learning and self-supervised learning, have further made pilotless reception possible as they have allowed the receivers to acquire channel information by simply observing signals. Further, novel intelligent wireless building of artificial intelligence and physical layer design has portrayed substantial gains of channel estimation precision, obstinacy to interference, and efficiency. This study is an intensive research study on implicit channel inference methods in case of pilo less OFDM reception among next-generation wireless networks. The proposed study is a hybrid framework that uses statistical signal modelling, blind estimation algorithms, and deep learning based inference to modify channel information without pilot symbols.

5G new radio (NR) introduced flexible numerology to provide the necessary flexibility for multiplexing the communication of heterogeneous services on a shared channel. One of the fundamental challenges of 5G NR is to develop resource allocation schemes to efficiently exploit such flexibility to optimize resource allocation of ultra-reliable low-latency communications (URLLC) in coexistence with enhanced mobile broadband (eMBB) while ensuring their colliding performance requirements. This problem of 5G NR resource allocation to URLLC services coexisted with eMMB services is an NP -hard problem. We present a new formulation of this problem by considering the interplay of eMBB and URLLC services’ constraints and targets. The objective of the formulated problem is to probabilistically meet quality of service (QoS) requirements of both eMBB and URLLC services when the optimal global solution is infeasible. To this end, we express the problem as an integer linear programming problem and consider two formulations with hard and soft URLLC throughput constraints. The overall problem is then treated as a specific instance of bin packing optimization, whose objective is to minimize the placements of URLLC services. We apply a greedy resource allocation algorithm to provide a near-optimal solution in polynomial-time to this problem. Finally, we numerically evaluate the optimal solutions of the two different formulations. Performance results demonstrate and confirm that the proposed 5G NR resource allocation solutions can provide efficient resource utilization while satisfying the performance requirements of the eMBB and URLLC services.

Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. In the proposed HNIDS, a PSO method improves the vector. GA is enhanced by adding a multi-objective function, which selects the best features and achieves improved fitness outcomes to explore the essential features and helps minimize dimensions, enhance the true positive rate (TPR), and lower the false positive rate (FPR). In the next phase, an IRF eliminates the less significant attributes, incorporates a list of decision trees across each iterative process, supervises the classifier’s performance, and prevents overfitting issues. The performance of the proposed method and existing ML methods are tested using the benchmark datasets NSL-KDD. The experimental findings demonstrated that the proposed HNIDS method achieves an accuracy of 98.979% on BCC and 88.149% on MCC for the NSL-KDD dataset, which is far better than the other ML methods i.e., SVM, RF, LR, NB, LDA, and CART.

The non-orthogonal coexistence between the enhanced mobile broadband (eMBB) and the ultra-reliable low-latency communication (URLLC) in the downlink of a multi-cell massive MIMO system is rigorously analyzed in this work. We provide a unified information-theoretic framework blending an infinite-blocklength analysis of the eMBB spectral efficiency (SE) in the ergodic regime with a finite-blocklength analysis of the URLLC error probability relying on the use of mismatched decoding, and of the so-called saddlepoint approximation. Puncturing (PUNC) and superposition coding (SPC) are considered as alternative downlink coexistence strategies to deal with the inter-service interference, under the assumption of only statistical channel state information (CSI) knowledge at the users. eMBB and URLLC performances are then evaluated over different precoding techniques and power control schemes, by accounting for imperfect CSI knowledge at the base stations, pilot-based estimation overhead, pilot contamination, spatially correlated channels, the structure of the radio frame, and the characteristics of the URLLC activation pattern. Simulation results reveal that SPC is, in many operating regimes, superior to PUNC in providing higher SE for the eMBB yet achieving the target reliability for the URLLC with high probability. Moreover, PUNC might cause eMBB service outage in presence of high URLLC traffic loads. However, PUNC turns to be necessary to preserve the URLLC performance in scenarios where the multi-user interference cannot be satisfactorily alleviated.

Ultra reliable low latency communication (URLLC) in vehicular networks is crucial for safety-related vehicular applications. Mini-slot with a short packet that carries only a few symbols is used to reduce the transmission time interval and enable quick scheduling for URLLC that requires extremely low latency. However, a single air interface transmission of URLLC packets may fail due to the high mobility of vehicles. Leveraging multi-connectivity technologies, the real-time reliability of URLLC can be greatly enhanced without relying on packet retransmission. In this paper, we propose a multi-connectivity URLLC downlink transmission scheme for vehicular networks, where the URLLC packet is duplicated and transmitted over multiple independent wireless links to improve packet reliability. Specifically, we design a multi-agent cooperative deep reinforcement learning algorithm, called transformer associated proximal policy optimization (TAPPO), to achieve real-time robust power allocation for multi-connectivity URLLC with imperfect channel state information (CSI). The transformer neural network architecture is employed to share the information among multiple links serving the same URLLC user and choose appropriate transmit powers, enabling cooperation to ensure reliability while minimizing inter-cell interference and energy consumption. Extensive simulation results validate the effectiveness of multi-connectivity packet duplication for URLLC and proposed TAPPO for power allocation.

Development of 5G system as a global telecommunication infrastructure is accelerating to realize the concept of a unified network infrastructure incorporating all access technologies. The potential of Low Earth Orbit (LEO) constellation systems has emerged to support wide range of services. This could help to achieve 5G key service requirements for enhanced Mobile Broadband (eMBB), Massive Machine-Type Communications (mMTC), and Ultra-Reliable Low-Latency Communication (URLLC). The integration of satellite communications with the 5G New Radio (NR) is stimulated by technology advancement to support challenging service requirements and demand for ubiquitous connectivity with the best possible quality of service. In this paper, we surveyed the opportunities of integrating terrestrial mobile and satellite networks, key technical challenges and proposed solutions. We also introduced a mobility management scheme that reduces signaling overhead and minimizes service interruption during inter satellite handover process.

Evolved UMTS terrestrial radio access network (EUTRAN) offers high uplink and downlink throughput. In this paper, we investigate the performance of five different schedulers of media access control for resource allocation, such as round robin (RB), proportional fair (PF), best channel quality indicator (CQI), max transmission point (TP) and resource fair (RF). On comparing the performance of resource allocation schedulers in the indistinguishable arrangement, we found the proportional fair (PF) offers balance in terms of individual metrics. Furthermore, we analyse the effect of feedback transmission time interval (TTI) delay on two typical EUTRAN networks referred to as long term evolution (LTE) and LTE-Advance (LTE-A). We evaluate the performance of the PF scheduler for both networks under varying feedback delay in TTI. Simulated results show that the delay in TTI affects the performance of resource allocation. Feedback delay improves network performance by compensating effect of imperfect channel.

The fifth-generation (5G) New Radio (NR) introduces stringent delay and reliability requirements to support diverse services, including enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). A major challenge is the efficient coexistence of these heterogeneous services, as eMBB demands high throughput while URLLC requires extreme reliability and minimal latency. This study investigates the coexistence of eMBB and URLLC under time-varying channel conditions and formulates a many-to-many URLLC resource allocation problem. To address this, we propose an opportunistic resource allocation scheme based on a genetic algorithm (GA) that dynamically optimizes resource block (RB) allocation for both services. The GA employs a heuristic fitness function designed to maximize eMBB fairness and throughput while ensuring URLLC reliability. Simulation results demonstrate that the proposed approach significantly improves overall system performance: eMBB fairness exceeds 95%, and the average eMBB data rate increases by approximately 500 Kbps compared to random allocation. Moreover, URLLC users maintain a consistent data rate of 600 Kbps, outperforming benchmark methods while satisfying the 99.999% reliability requirement. The results confirm that the proposed GA-based approach effectively balances throughput, fairness, and reliability, making it a promising solution for future 5G networks with mixed traffic demands.

Significant demands for massive machine-type communication (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communication (URLLC) have been brought about by the introduction of 5G New Radio (NR) and the impending B5G/6G technologies. In high-speed mobile scenarios, ensuring seamless handover (HO) with minimal latency and packet loss remains a persistent challenge. Traditional static or rule-based approaches to mobility management often fail to scale in dense and dynamic network conditions. To address this we propose an advanced framework, termed DeepEdge-LIM<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (Learning-based Intelligent Mobility Management), which leverages edge computing and deep reinforcement learning (DRL) for dynamic handover optimization. Specifically, the proposed system utilizes a dual-stage intelligence: (1) A Kalman filter coupled with fuzzy logic to accurately forecast signal quality fluctuations of the serving and neighboring cells, and (2) A deep Q-learning network (DQN) deployed at the edge nodes to adaptively control the Hysteresis Margin, Time-to-Trigger ensuring real-time decision making. The action selection policy incorporates an improved ε-greedy strategy with decaying exploration and a reward function tailored for latency minimization, signal strength stability, and throughput enhancement. We implement and simulate DeepEdge-LIM<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> using the NS-3 and OpenAI Gym framework. Our suggested approach is appropriate for upcoming intelligent 5G and 6G deployments since evaluation results show that it significantly outperforms traditional and learning-based baselines in lowering handover failures, ping-pong effects, and HO latency, especially in ultra-dense and high-speed mobility scenarios.

ETSI DECT-2020 New Radio (NR) is a new flexible radio interface targeted to support a broad range of wireless Internet of Things (IoT) applications. It has been shown to fulfill both massive machine-type communications (mMTC) and ultra-reliable low latency communications (URLLC) requirements for 5th generation (5G) networks. DECT-2020 NR is an autonomous wireless mesh network protocol where the devices can choose to become routers, forwarding the data of other devices in addition to their own data. Thus, a wireless mesh network does not need separate base stations or a core network architecture to extend coverage. This makes the deployment of DECT-2020 NR networks easy, but with the cost of increased energy consumption in the router nodes. Notably, the same energy consumption is not inflicted upon the non-routing leaf nodes whose operation is on the contrary very energy efficient. This role-induced disparity in node energy consumption results in the network not using the energy of its devices with maximal efficiency. A method to alleviate this problem could be a network rotation in which the roles of network nodes are periodically rotated and the energy consumption is thus distributed more evenly among the nodes. In this paper we propose a role rotation protocol for DECT-2020 NR and evaluate its impact to the expected lifetime and the performance of the network by system level simulations.

A three-level slicing algorithm in a multi-slice multi-numerology context