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Next generation of wireless networks are characterized by two main features named Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). These two services can be accommodated in the same wireless infrastructure so that wide range of users, demanding either massive throughput or extremely low latency and high reliability requirements, are directly benefited for providing various mission critical services. Co-existence of eMBB and URLLC services, however, demand highly efficient and less complex resource allocation schemes. In this paper, various resource allocation techniques are studied for the co-existence of eMBB and URLLC traffic to meet the heterogeneous specifications of each class of users. A detailed study on existing resource allocation schemes for simultaneous transmission of eMBB and URLLC services based on network slicing, flexible Transmit Time Interval (TTI), scheduling and distributed and federated learning are provided. Moreover, Machine Learning (ML) aided and Reconfigurable Intelligent Surface (RIS) and Unmanned Aerial Vehicle (UAV) assisted resource allocation techniques are also studied in detail. Additionally, this paper identifies some challenges for eMBB and URLLC service accommodations in the same wireless architecture, and proposes their possible solution approaches.

In the era of 5G and beyond wireless networks, the simultaneous support of enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) poses significant challenges in managing radio resources efficiently. By leveraging the puncturing technique, we propose an intelligent resource management framework for meeting the strict latency and reliability requirement of URLLC services and the high data rate for eMBB services. In particular, a semi-supervised learning and deep reinforcement learning (DRL) based architecture is proposed to manage the resources intelligently. We decompose the optimization problem into two subproblems: 1) resource block allocation (RBA) strategy for eMBB slice, and 2) URLLC scheduling. Through extensive simulations and performance evaluations, we demonstrate the effectiveness of the proposed technique in optimizing resource utilization, minimizing latency for URLLC users, and maximizing the throughput for eMBB services. Simulation findings demonstrate that the proposed methodology can ensure the URLLC reliability requirements while maintaining higher average sum rate for eMBB and higher convergence rate. The proposed framework paves the way for the efficient coexistence of diverse services, enabling wireless network operators to optimize resource allocation, improve user experience, and meet the specific requirements of eMBB and URLLC applications.

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.

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two services on the same wireless infrastructure leads to a challenging resource allocation problem due to their heterogeneous specifications. To address this problem, slicing has emerged as an architecture that enables a logical network with specific radio access functionality to each of the supported services on the same network infrastructure. The allocation of radio resources to each slice according to their requirements is a fundamental part of the network slicing that is usually executed at the radio access network (RAN). In this work, we formulate the RAN resource allocation problem as a sum-rate maximization problem subject to the orthogonality constraint (i.e., service isolation), latency-related constraint and minimum rate constraint while maintaining the reliability constraint with the incorporation of adaptive modulation and coding (AMC). However, the formulated problem is not mathematically tractable due to the presence of a step-wise function associated with the AMC and a binary assignment variable. Therefore, to solve the proposed optimization problem, first, we relax the mathematical intractability of AMC by using an approximation of the non-linear AMC achievable throughput, and next, the binary constraint is relaxed to a box constraint by using the penalized reformulation of the problem. The result of the above two-step procedure provides a close-to-optimal solution to the original optimization problem. Furthermore, to ease the complexity of the optimization-based scheduling algorithm, a low-complexity heuristic scheduling scheme is proposed for the efficient multiplexing of URLLC and eMBB services. Finally, the effectiveness of the proposed optimization and heuristic schemes is illustrated through extensive numerical simulations.

The fifth generation (5G) of wireless networks features three core use cases, namely ultra-reliable and low latency communications (URLLC), massive machine type communications (mMTC), and enhanced mobile broadband (eMBB). These use cases co-exist in many practical scenarios and compete for the same set of time and frequency resources, resulting in a natural trade-off in their performance. In this paper, a network supporting both URLLC and eMBB modes of operation is studied. To guarantee the ultra low latency requirement of URLLC, a dynamic resource allocation scheme indicated by a two-dimensional bitmap is proposed. This approach is capable to achieve finer granularity as well as lower false cancellation rate compared to the state-of-the-art methods. A novel power control and indication method is also proposed to dynamically provide different power control parameters to the user equipment (UE), while guaranteeing the reliability requirement of URLLC and minimizing the impact to eMBB. In addition, we devise a dynamic selection mechanism (DSM) to accommodate diverse scenarios, which is empowered with load prediction to become more intelligent. Our extensive system-level simulation results for eMBB-URLLC co-existence scenarios showcase that the perceived throughput of eMBB UEs is increased by 45.3%, while about 13.3% more UEs are enjoying URLLC services with at most 84% transmit power savings compared to the state-of-the-art methods.

In 5G/B5G communication systems, network slicing is utilized to tackle the problem of the allocation of network resources for diverse services with changing demands. We proposed an algorithm that prioritizes the characteristic requirements of two different services and tackles the problem of allocation and scheduling of resources in the hybrid services system with eMBB and URLLC. Firstly, the resource allocation and scheduling are modeled, subject to the rate and delay constraints of both services. Secondly, the purpose of adopting a dueling deep Q network (Dueling DQN) is to approach the formulated non-convex optimization problem innovatively, in which a resource scheduling mechanism and the ϵ-greedy strategy were utilized to select the optimal resource allocation action. Moreover, the reward-clipping mechanism is introduced to enhance the training stability of Dueling DQN. Meanwhile, we choose a suitable bandwidth allocation resolution to increase flexibility in resource allocation. Finally, the simulations indicate that the proposed Dueling DQN algorithm has excellent performance in terms of quality of experience (QoE), spectrum efficiency (SE) and network utility, and the scheduling mechanism makes the performance much more stable. In contrast with Q-learning, DQN as well as Double DQN, the proposed algorithm based on Dueling DQN improves the network utility by 11%, 8% and 2%, respectively.

Multi-access edge computing (MEC) enables mobile users to offload their computation tasks to the server located at the edge of the cellular network. Thereby, MEC prolongs the battery lifespan of mobile devices and significantly enhances their computation capacities. However, a significant challenge is to offload the computation tasks to the MEC server in an energy-efficient manner. Meanwhile, in the fifth-generation (5G) networks, mobile users with different service requirements classified as enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users will coexist in the current cellular network. Therefore, it is important to appropriately multiplex these users in the cellular network. In this work, we address the issues of these two promising technologies together. Firstly, we formulate an energy-efficient task offloading, and scheduling of eMBB and URLLC users as a mixed-integer non-linear problem. Then, we decompose the problem into multiple sub-problems in order to transform into convex form and alternately solve them until converging to the desired solutions by using the block coordinate descent (BCD) algorithm. Finally, we demonstrate numerical results to prove the superior effectiveness in the performance of our proposed algorithm over classical existing schemes.

The major use cases for IMT-2020 include enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). URLLC packets occur sporadically, but each time, available resources with favorable conditions are required for their timely and trusted transmissions. To this end, an overlapping scheme of puncturing a part of resources scheduled to an eMBB packet for URLLC packets is proposed. Such a scheme causes inevitable damages to eMBB packets. Thus, additional delays due to possible retransmissions of damaged packets should be considered in the calculation of spectrum requirement for IMT-2020. So far, the method provided by International Telecommunication Union Radiocommunication Sector (ITU-R) based on an M/G/1 non-preemptive priority queuing model has been widely used in calculating spectrum requirements for mobile broadband services. In this work, we extend it to reflect the puncturing of URLLC on eMBB. We fully consider additional delays due to retransmissions by exploiting the concept of delay cycle. Numerical simulations validate the accuracy of our analytical results. Furthermore, we provide an algorithm to calculate spectrum requirement for IMT-2020 using the same input parameters as in the method of ITU-R.

The services of enhanced mobile broadband (eMBB) and the ultra-reliable low-latency communication (URLLC) enabled by 5G new radio (NR) are considered as the essential prerequisites of the future intelligent transportation systems. Particularly, the eMBB service is designed to provide extremely high data rate for content delivery and thereby can significantly enhance the quality of experience (QoE) of the bandwidth-hungry in-vehicle entertainment applications. On the other hand, the URLLC is designed to tackle the stringent requirements on the latency and reliability of the critical packets transmissions, and thereby can facilitate the autonomous driving of the connected vehicles. In this paper, under the scenario of a one-way highway vehicular network, we address the issue of joint resource allocations of the eMBB and URLLC traffics for the enhancement of the network performance. The eMBB traffic is assumed to be full-buffered and scheduled at the boundary of each time slot for multimedia transmissions. During each eMBB transmission interval, random arrivals of URLLC traffics are assumed. To satisfy the latency constraint of the URLLC transmissions, the eMBB time slot is divided into minislots and the newly arrived URLLC traffic is promptly scheduled in the next minislot by puncturing the on-going eMBB traffics. Further, to guarantee the reliability of the URLLC traffic, guard zones are deployed around the vehicle receivers as protections and the eMBB transmissions inside the guard zones are prohibited. Under this context, we first derive the transmission probability of the RSU nodes. Then, based on the obtained results, we capture association probabilities of the vehicle receivers for URLLC and eMBB traffics, respectively. Finally, the coverage performance of the vehicle-to-vehicle (V2V) links and the rate coverage performance of the vehicle-to-infrastructure (V2I) links are analyzed.

The enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC) are the two main scenarios of 5th generation (5G) mobile communication system networks. There is an obvious difference in service requirements between different scenarios. When multi-scenario services coexist in the 5G networks, exploring optimized resource scheduling and allocation strategies become a critical issue. The 5G New Radio (NR) and numerology technologies have been standardized, which lay the foundation for flexible frame structure and adaptive scheduling. In this paper, we propose the self-adaptive flexible transmission time interval (TTI) scheduling (SAFE-TS) strategy in the eMBB and uRLLC coexistence scenario. Machine learning (ML) is applied to achieve flexible TTI scheduling. Moreover, we design the random forest-based ensemble TTI decision algorithm (RF-ETDA) to accomplish the TTI selection for each service. Compared with the existing ML methods, the proposed algorithm has a performance improvement in selecting TTI, especially for the uRLLC services. Then, the TTI selection results will be the basis of system resource scheduling and allocation. The simulation results prove that the proposed SAFE-TS effectively reduce the delay and packet loss rate of the uRLLC services while guaranteeing the eMBB requirements. Therefore, it is highly recommended that flexible TTI scheduling should be applied in the construction of the 5G networks to achieve superior network performances.

In 5G/B5G communication systems, network slicing is utilized to tackle the problem of the allocation of network resources for diverse services with changing demands. We proposed an algorithm that prioritizes the characteristic requirements of two different services and tackles the problem of allocation and scheduling of resources in the hybrid services system with eMBB and URLLC. Firstly, the resource allocation and scheduling are modeled, subject to the rate and delay constraints of both services. Secondly, the purpose of adopting a dueling deep Q network (Dueling DQN) is to approach the formulated non-convex optimization problem innovatively, in which a resource scheduling mechanism and the ϵ-greedy strategy were utilized to select the optimal resource allocation action. Moreover, the reward-clipping mechanism is introduced to enhance the training stability of Dueling DQN. Meanwhile, we choose a suitable bandwidth allocation resolution to increase flexibility in resource allocation. Finally, the simulations indicate that the proposed Dueling DQN algorithm has excellent performance in terms of quality of experience (QoE), spectrum efficiency (SE) and network utility, and the scheduling mechanism makes the performance much more stable. In contrast with Q-learning, DQN as well as Double DQN, the proposed algorithm based on Dueling DQN improves the network utility by 11%, 8% and 2%, respectively.

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two core services of the emerging fifth-generation (5G) systems. URLLC services require lower latency and high reliability, whereas eMBB services expect maximum data rates. Since 5G new radio (NR) aims to support heterogeneous services, the 3rd generation partnership project (3GPP) supports a highly flexible frame structure by introducing mixed numerology and mini-slot approaches. In these systems, the time slot is further divided into mini-slots. The coexistence of these two heterogeneous services leads to a challenging resource allocation problem. In this paper, we study the joint resource allocation problem of eMBB and URLLC schedulers to maximize the minimum expected achieved rate (MEAR) of eMBB users while satisfying URLLC users’ quality of service (QoS) constraints. The resource allocation of eMBB traffic occurs at time slot boundaries. In contrast, URLLC traffic is overlapped at the mini-slot timescale by adopting the non-orthogonal multiple access (NOMA) superposition technique. The suitable pairs of eMBB, URLLC users are evaluated using the one-one matching theory by inculcating fairness among eMBB users while satisfying the QoS parameters of URLCC users. The significance of the proposed approach over the baseline approaches is evaluated through numerical simulations in terms of the MEAR, mixed numerologies, and fairness scores among eMBB users. [ABSTRACT FROM AUTHOR]

In this paper, we study the joint resource scheduling problem for multiplexing two distinct 5 G services: enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC). While eMBB services require high data rates, URLLC services lay more emphasis on extremely low latency and high reliability. To satisfy these heterogeneous requirements for eMBB and URLLC coexistence, we propose a risk-resistant scheme for resource allocation to both eMBB and URLLC traffic that aims to maximize the eMBB data rate, while considering the URLLC delay threshold violation to maintain URLLC delay requirements. Based on the M/G/1 queueing model, the URLLC packet latency target is realized by minimizing the average packet delay in violation cases, where “violation” is judged by introducing the “conditional value at risk (CVaR)” as a risk metric (i.e., “risk” here is defined as URLLC delay threshold violation). To solve the formulated problem, we transform it into two convex optimization problems to obtain a near-optimal resource allocation solution. Simulation results show that the proposed scheme improves the fairness among the eMBB users and eMBB throughput by $30\%\sim 34\%$ compared to existing works, while satisfying the stringent URLLC delay requirements. [ABSTRACT FROM AUTHOR]

Ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) are two influential services of the emerging 5G cellular network. Latency and reliability are major concerns for uRLLC applications, whereas eMBB services claim for the maximum data rates. Owing to the trade-off among latency, reliability and spectral efficiency, sharing of radio resources between eMBB and uRLLC services, heads to a challenging scheduling dilemma. In this paper, we study the co-scheduling problem of eMBB and uRLLC traffic based upon the puncturing technique. Precisely, we formulate an optimization problem aiming to maximize the minimum expected achieved rate (MEAR) of eMBB user equipment (UE) while fulfilling the provisions of the uRLLC traffic. We decompose the original problem into two sub-problems, namely scheduling problem of eMBB UEs and uRLLC UEs while prevailing objective unchanged. Radio resources are scheduled among the eMBB UEs on a time slot basis, whereas it is handled for uRLLC UEs on a mini-slot basis. Moreover, for resolving the scheduling issue of eMBB UEs, we use penalty successive upper bound minimization (PSUM) based algorithm, whereas the optimal transportation model (TM) is adopted for solving the same problem of uRLLC UEs. Furthermore, a heuristic algorithm is also provided to solve the first sub-problem with lower complexity. Finally, the significance of the proposed approach over other baseline approaches is established through numerical analysis in terms of the MEAR and fairness scores of the eMBB UEs. [ABSTRACT FROM AUTHOR]

In this paper, we studied resource allocation in a dynamic multiplexing scenario of two 5G services, namely, enhanced mobile broadband (eMBB) and ultrareliable low-latency communication (URLLC). Considering that eMBB services focus on high data rates while URLLC is very strict in terms of latency and reliability, we formulate a joint eMBB/URLLC resource allocation problem. To solve the formulated problem, in particular, we propose a joint resource allocation algorithm that can satisfy both eMBB user rate and URLLC interrupt probability requirements. We propose an RB allocation scheme that takes into account the needs of eMBB and URLLC users. The program consists of two phases as follows: (1) establish an RB allocation for eMBB users and (2) assign the minislot for URLLC users and calculate the transmission power of URLLC users that ensures the reliability constraint. We also realize and verify the algorithm through the simulation. [ABSTRACT FROM AUTHOR]

Enhanced mobile broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC) are the two main services in the fifth-generation mobile network. URLLC services have stringent latency and reliability requirements, while eMBB services are designed to provide extremely high data rates for content delivery. The differentiated Quality of Service (QoS) requirements of the two services make their coexistence on the same bandwidth resources a challenging issue. To meet this challenge, we introduce reconfigurable intelligent surface (RIS) technology to assist in solving the resource allocation problem. The problem is formulated as two optimization problems. For the eMBB allocation problem, we jointly optimize the resource block allocation, power allocation, and RIS phase shift matrix and propose a two-stage alternating iterative algorithm to maximize the total traversal capacity of eMBB users. For the URLLC allocation problem, we maximize the URLLC packet reception rate and minimize the amount of eMBB rate loss while ensuring the quality of service for eMBB and URLLC. A heuristic URLLC allocation algorithm based on pre-configured RIS is proposed. Simulation results show that the proposed algorithm can meet the strict delay requirements of URLLC even when the URLLC packet reception rate is nearly 95.5%. Meanwhile, the total loss rate of eMBB service caused by the reuse of URLLC service is less than 6%. Thus, the applicability of the proposed solution to the coexistence problem is demonstrated. [ABSTRACT FROM AUTHOR]

The dynamic resource block structure (D-RBS) allows for flexible allocation of radio resources. This flexibility (potentially) enables efficient utilization of available resources and adaptability to changing network conditions. In this context, managing resource contention and optimizing allocation decisions become increasingly challenging. In this research paper, we introduce a new approach for D-RBS for re-allocation of enhanced Mobile Broadband (eMBB) and massive Machine Type Communication (mMTC) resource blocs (RBs) to URLLC users. Our scheme leverages artificial intelligence (AI) to support the three main services of 5th generation networks. To efficiently allocate resources for eMBB/mMTC and URLLC services, we propose an intelligent puncturing scheme. Additionally, we formulate an optimization problem that aims to minimize resource and transmit power usage while meeting the requirements of all three services. Since this problem is non-convex and involves multiple optimization variables, we utilize deep reinforcement learning as a solution algorithm. We then compare our proposed intelligent allocation (IA) scheme with two other schemes: random allocation (RaA) and overallocation (OA), which have lower complexity and overhead. Performance and complexity analyses are conducted in a multi-cell scenario with interference. Our results demonstrate that the IA scheme outperforms RaA and OA, achieving an energy efficiency gain of 40% and 15% respectively. However, it is worth noting that IA has a 36.3% higher complexity in terms of action selection compared to RaA and OA.

As the manufacturing industry gradually expands and tends to be intelligent, the industrial networks have been greatly applied and are of paramount importance. Furthermore, taking the advantages of wireless networks into account, such as flexible deployment, low cost, and easy maintenance, etc., this paper is dedicated to the research of industrial wireless networks. Facing the problem of limited radio resource, in order to make full use of frequency band, this paper proposes a resource scheduling scheme with puncturing, which guarantees the reliability of subsequent ultra-reliable and low-latency communications (URLLC) services under the conditions of existing enhanced mobile broadband (eMBB) services. By optimizing the bandwidth pre-allocation for eMBB services, puncture weight and transmit power, this scheme can effectively meet the needs of the system. Specifically, the purpose of this paper is to minimize the decoding error rate of the devices carrying URLLC service while ensuring the demand for throughput of eMBB services. The problem proposed is a nonlinear stochastic optimization problem involving complex Q-function. The closed-form expression of the objective function is obtained through linear approximation and probability theory, which is further decoupled into three sub-problems. To this end, a block coordinate descent optimization (BCDO) algorithm is proposed to obtain the optimal bandwidth allocation, puncture weight and transmit power. The simulation results verify the rationality of the theoretical analysis and the effectiveness of the proposed algorithm, and also elaborate the influence of different parameters on the decoding error rate of the URLLC devices.

This paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices. We take the network slicing technology to solve the problems of network spectral efficiency and URLLC reliability. A mixed-integer programming problem is formulated by maximizing the spectral efficiency of the system in the constraint of users' requirements for two slices, i.e., the requirement of the eMBB slice and the requirement of the URLLC slice with a high probability for each user. By transforming and relaxing integer variables, the original problem is approximated to a convex optimization problem. Then, we combine the objective function and the constraint conditions through dual variables to form an augmented Lagrangian function, and the optimal solution of this function is the upper bound of the original problem. In addition, we propose a resource allocation algorithm that allocates the network slicing by applying the Powell–Hestenes–Rockafellar method and the branch and bound method, obtaining the optimal solution. The simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm. Furthermore, we analyze the spectral efficiency of the proposed algorithm with the users' requirements change of two slices and get better spectral efficiency performance. [ABSTRACT FROM AUTHOR]

One of the core features of 5G networks is the ability to support multiple services on the same infrastructure, with network slicing being a key technology. However, existing network slicing architectures have limitations in efficiently handling slice requests with different requirements, particularly when addressing high-reliability and high-demand services, where many issues remain unresolved. For example, predicting whether actual physical resources can meet network slice request demands and achieving flexible, on-demand resource allocation for different types of slice requests are significant challenges. To address the need for more flexible and efficient service demands, this paper proposes a 5G network slicing deployment algorithm based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Firstly, a new 5G network slicing deployment system framework is established, which measures resources for three typical 5G network slicing scenarios (eMBB, mMTC, uRLLC) and processes different types of slice requests by predicting slice request traffic. Secondly, by adopting the multi-agent approach of MADDPG, the algorithm enhances cooperation between multiple service requests, decentralizes action selection for requests, and schedules resources separately for the three types of slice requests, thereby optimizing resource allocation. Finally, simulation results demonstrate that the proposed algorithm significantly outperforms existing algorithms in terms of resource efficiency and slice request acceptance rate, showcasing the advantages of multi-agent approaches in slice request handling. [ABSTRACT FROM AUTHOR]

Ultra Reliable and Low Latency Communications (URLLC) is deemed to be an essential service in 5G systems and beyond (also called 6G) to accommodate a wide range of emerging applications with stringent latency and reliability requirements. Coexistence of URLLC alongside other service categories calls for developing spectrally efficient multiplexing techniques. Specifically, coupling URLLC and conventional enhanced Mobile BroadBand (eMBB) through superposition/puncturing naturally arises as a promising option due to the tolerance of the latter in terms of latency and reliability. The idea here is to transmit URLLC packets (typically sporadic and of short size) over resources occupied by ongoing eMBB transmissions while minimizing the impact on the eMBB transmissions. In this paper, we propose a novel downlink URLLC-eMBB multiplexing technique that exploits possible similarities among URLLC and eMBB symbols, with the objective of reducing the size of the punctured eMBB symbols. We propose that the base station (BS) scans the eMBB traffic’ symbol sequences and punctures those that have the highest symbol similarity with that of the URLLC users to be served. As the eMBB and URLLC may use different constellation sizes, we introduce the concept of symbol region similarity to accommodate the different constellations. We assess the performance of the proposed scheme analytically, where we derive closed-form expressions for the symbol error rate (SER) of the eMBB and URLLC services. We also derive an expression for the eMBB loss function due to puncturing in terms of the eMBB SER. We demonstrate through numerical and simulation results the efficacy of the proposed scheme where we show that 1) the eMBB spectral efficiency is improved by puncturing fewer symbols, 2) the SER and reliability performance of eMBB are improved, and 3) the URLLC data is accommodated within the specified delay constraint while maintaining its reliability, 4) and the proposed strategy has linear time complexity making it an efficient solution to be used in practice. [ABSTRACT FROM AUTHOR]

This paper studies the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services in a cellular network that is assisted by a reconfigurable intelligent surface (RIS). The system model consists of one base station (BS) and one RIS that is deployed to enhance the performance of both eMBB and URLLC in terms of the achievable data rate and reliability, respectively. We formulate two optimization problems, a time slot basis eMBB allocation problem and a mini-time slot basis URLLC allocation problem. The eMBB allocation problem aims at maximizing the eMBB sum rate by jointly optimizing the power allocation at the BS and the RIS phase-shift matrix while satisfying the eMBB rate constraint. On the other hand, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss. This is achieved by jointly optimizing the power and frequency allocations along with the RIS phase-shift matrix. In order to avoid the violation in the URLLC latency requirements, we propose a novel framework in which the RIS phase-shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. For the sake of solving the URLLC allocation problem, two algorithms are proposed, namely, an optimization-based URLLC allocation algorithm and a heuristic algorithm. The simulation results show that the heuristic algorithm has a low time complexity, which makes it practical for real-time and efficient multiplexing between eMBB and URLLC traffic. In addition, using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB sum rate. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) relay networks are convinced to be a significant complement to terrestrial infrastructures to provide robust network capacity. However, most of the existing works either considered enhanced mobile broadband (eMBB) payload communication or ultra-reliable and low latency communications (URLLC) control information communication. In this paper, we investigate resource allocation for the eMBB payload and URLLC control information communication multiplexing in a multi-UAV relay network. We firstly propose a multi-UAV relay model comprehensively considering path loss, small-scale channel fading and different quality of service requirements of eMBB and URLLC communications. Then we formulate the multiplexing problem as a joint user association, bandwidth and transmit power optimization problem to improve total transmission data rate and reduce power consumption. The solution of this problem is challenging due to different capacity characteristics of eMBB and URLLC communications, the coupling of continuous variables and integer variables, and the non-convexity. To mitigate these challenges, we equivalently decompose the original optimization problem into a URLLC problem and an eMBB problem. For the URLLC problem, we derive closed-form expressions of the optimal bandwidth and transmit power. For the eMBB problem, we develop an iterative solution framework of alternatively optimizing user association, bandwidth and transmit power. [ABSTRACT FROM AUTHOR]

With the rapid development of the 5G power Internet of Things (IoT), new power systems have higher requirements for data transmission rates, latency, reliability, and energy efficiency. Specifically, the hybrid service of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) has brought new challenges to the differentiated service of the 5G power IoT. To solve the above problems, this paper first constructs a power IoT model based on NOMA for the mixed service of URLLC and eMBB. Considering the shortage of resource utilization in eMBB and URLLC hybrid power service scenarios, the problem of maximizing system throughput through joint channel selection and power allocation is proposed. The channel selection algorithm based on matching as well as the power allocation algorithm based on water injection are developed to tackle the problem. Both theoretical analysis and experimental simulation verify that our method has superior performance in system throughput and spectrum efficiency. [ABSTRACT FROM AUTHOR]

Packet-switched xHaul networks are a scalable solution enabling convergent transport of diverse types of radio data flows, such as fronthaul / midhaul / backhaul (FH / MH / BH) flows, between remote sites and a central site (hub) in 5G radio access networks (RANs). Such networks can be realized using the cost-efficient Ethernet technology, which enhanced with time-sensitive networking (TSN) features allows for prioritized transmission of latency-sensitive fronthaul flows. Provisioning of multiple types of 5G services of different service requirements in a shared network, commonly referred to as network slicing, requires adequate handling of transported data flows in order to satisfy particular service / slice requirements. In this work, we investigate two traffic prioritization policies, namely, flowaware (FA) and latency-aware (LA), in a packet-switched xHaul network supporting slices of different latency requirements. We evaluate the effectiveness of the policies in a networkplanning case study, where virtualized radio processing resources allocated at the processing pool (PP) facilities, for two slices related to enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) services, are subject to optimization. Using numerical experiments, we analyze PP cost savings from applying the LA policy (vs. FA) in various network scenarios. The savings in active PPs reach up to 40% - 60% in ring scenarios and 30% in a mesh network, whereas the gains in overall PP cost are up to 20% for the cost values assumed in the analysis. [ABSTRACT FROM AUTHOR]

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication. [ABSTRACT FROM AUTHOR]

Network slicing makes it possible for future applications with a variety of adaptability requirements and performance requirements by spliting the physical network into several logical networks. Radio access network (RAN) slicing's main goal is to assign physical resource blocks (RBs) to mMTC, eMBB, and uRLLC services while ensuring the Quality of service (QoS). Consequently, it is challenging to determine the optimal strategies for 5G radio access network (5G-RAN) slicing because of dynamically changes in slice needs and environmental data, and conventional approaches have difficulty addressing resource allocation issues. In this paper, we present an energy-efficient deep deterministic policy gradient resource allocation (EE-DDPG-RA) method for RAN slicing in 5G networks to choose the resource allocation policy that increases long-term throughput while satisfying the requirements of B5G systems for quality of service. This method's main goal is to remove unnecessary actions in order to lower the amount of available action space. The numerical outcomes demonstrate that the proposed approach outperforms boundaries by enhancing deep-rooted throughput and effectively managing resources. [ABSTRACT FROM AUTHOR]

5G cellular networks are designed to address the need for increased bandwidth of end users (eMBB services), high-reliability and low-latency of critical industrial use cases (URLLC), and long-range, low-power connectivity of machine-type IoT applications (mMTC). To meet such contrasting requirements, 5G networks rely on its new radio interface (5G-NR) to address eMBB and URLLC services and on LTE-M and NB-IoT for mMTC services. As of Release 16, 5G networks effectively support the extreme ends of key performance indicators, namely bandwidth, reliability, latency, coverage, and autonomy. This article analyzes the gap between those capabilities and the requirements of use cases as discussed in the NR-REDCAP 3GPP Study Item of Release 17: video surveillance, industrial IoT, and wearables, to name a few. This article further discusses the challenges driving the design of this new air interface for reduced-capability 5G devices in the sub-6 GHz band and key enablers to fulfill those use case requirements. [ABSTRACT FROM AUTHOR]

5G New Radio (NR) is envisioned to efficiently support both enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). Due to the severe constraint of URLLC traffic, the time resources used by eMBB are further divided into mini-slots. From the media access control (MAC) layer scheduling perspective, the resource allocation occurs on different timescales, and URLLC traffic is pre-emptively overlapped at the mini-slot timescale to obtain the strict requirements of URLLC (achieve 99.999% reliability within 1 ms latency). In this article, we analyze the multiplexing of eMBB and URLLC traffic in 5G downlink transmission, with the dual objectives of maximizing eMBB utility like proportional fairness for eMBB users while satisfying URLLC constraints. We formulate the resource allocation problem in each mini-slot as the integer programming (IP) problem with two solutions: 1) convex relaxation; 2) greedy algorithm. The simulation results show that our algorithms have a higher utility of eMBB users while satisfying the URLLC users’ latency and reliability requirements than using basic round robin. The performance comparison between the two algorithms is extensively studied, and the choice of two algorithms in practice can be decided by the trade-off analysis investigated in this article. [ABSTRACT FROM AUTHOR]

Multiservice cellular in Radio Access Network (RAN) Slicing has recently attained huge interest in enhancing isolation and flexibility. However, RAN slicing in heterogeneous networks (HetNet) architecture is not adequately explored. This study proposes a pairing-network slicing (NS) approach for Multiservice RAN that cares about quality of service (QoS), baseband resources, capacities of wireless fronthaul and backhaul links, and isolation. This intriguing approach helps address the increased need for mobile network traffic produced by a range of devices with various QoS requirements, including improved dependability, ultra-reliability low-latency communications (uRLLC), and enhanced broadband Mobile Services (eMBB). Our study displays a unique RAN slicing framework for user equipment (UE) for joint user-association. Multicell non-orthogonal multiple access (NOMA)-based resource allocation across 5G HetNet under successive interference cancelation (SIC) is seen to achieve the best performance. Joint user-slice pairing and association are optimization problems to maximize eMBB UE data rates while fulfilling uRLLC latency and reliability criteria. This is accomplished by guaranteeing the inter- and intra-isolation property of slicing to eliminate interferences between eMBB and uRLLC slices. We presented the UE-slice association (U-S. A) algorithm as a one-to-many matching game to create a stable connection between UE and one of the base stations (BSs). Next, we use the UE-slice pairing (U-S. P) algorithm to find stable uRLLC-eMBB pairs that coexist on the same spectrum. Numerical findings and performance analyses of the submitted association and pairing technique show they can all be RAN slicing criteria. We prove that the proposed algorithm optimizes system throughput while decreasing uRLLC latency by associating and pairing every uRLLC user in mini slots. [ABSTRACT FROM AUTHOR]

With the explosive growth of network traffic and the diversification of service demands, network slicing (NS) and dual connectivity (DC) are considered as promising technologies in wireless networks. In this paper, we propose a novel algorithm that solves the resource allocation problem of NS in heterogeneous networks with the assistance of DC, while satisfying the characteristic requirements of eMBB and URLLC services. Firstly, we model the scenario and formulate the optimization problem, which is proved as an NP-Hard problem. Secondly, due to the nonconvex and combinatorial nature, the dueling double deep Q-network with long short-term memory (LSTM-D3QN) is proposed to solve this problem, aiming to improve the overall network utility, while ensuring the quality of experience (QoE). Then, we analyze the complexity of the algorithm. Finally, the simulation results show that the proposed algorithm can maximize the total utility of the system, while guaranteeing the user QoE. Compared with LSTM-A2C and DQN, the proposed algorithm improves the long-term network utility by 2.6% and 7.2%, respectively. In addition, compared with the algorithm without DC under the conditions of no priority, eMBB priority and URLLC priority, the proposed algorithm improves the network utility by 4.2%, 2.1% and 4.1%, respectively. [ABSTRACT FROM AUTHOR]

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other services, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system in the presence of eMBB traffic. Based on the proposed superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the rate loss of the eMBB service and URLLC packet segmentation loss while satisfying the eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed-integer non-linear program (MINLP) which is generally very hard to solve in polynomial time. Hence, we reformulate the problem as a one-to-one pairing problem and we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to support the many-to-many pairing. Simulation results show that the proposed algorithm achieves higher URLLC packet admission rate and lower rate loss for eMBB. For instance, the URLLC packet admission rate, unlike baseline methods, is shown to be preserved under the proposed method even at higher URLLC load. It is shown that at least 30% more URLLC users can be served without degrading their QoS, while keeping the impact on eMBB rate minimal. Detailed numerical evaluation is presented to quantify the benefits of the proposed method. [ABSTRACT FROM AUTHOR]

Fifth generation mobile communication systems (5G) have to accommodate both Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) services. While eMBB applications support high data rates, URLLC services aim at guaranteeing low-latencies and high-reliabilities. eMBB and URLLC services are scheduled on the same frequency band, where the different latency requirements of the communications render their coexistence challenging. In this survey, we review, from an information theoretic perspective, coding schemes that simultaneously accommodate URLLC and eMBB transmissions and show that they outperform traditional scheduling approaches. Various communication scenarios are considered, including point-to-point channels, broadcast channels, interference networks, cellular models, and cloud radio access networks (C-RANs). The main focus is on the set of rate pairs that can simultaneously be achieved for URLLC and eMBB messages, which captures well the tension between the two types of communications. We also discuss finite-blocklength results where the measure of interest is the set of error probability pairs that can simultaneously be achieved in the two communication regimes. [ABSTRACT FROM AUTHOR]

Network slicing and mobile edge computing are key technologies in 5G/6G networks. Stable allocation of slicing resources is a challenge while communication and computation offloading coexist. To meet the service requirements of communication and computation offloading, a radio access network model which consists of enhanced mobile broadband (eMBB) slicing users, ultra‐reliable low latency communication (URLLC) slicing users, base stations integrated with MEC servers, and fog nodes with computing power are proposed. By employing the Lyapunov optimisation technique, a slicing resource allocation algorithm is proposed to maintain the queue stability and minimise the energy consumption of the system. For eMBB and URLLC slices, long time interval and short time interval resource allocation algorithms are proposed respectively. Simulation results show that the proposed algorithm can guarantee the long‐term stability of the network system while ensuring the service requirements. [ABSTRACT FROM AUTHOR]

Fifth generation (5G) networks support ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). The coexistence of URLLC and eMBB is often organized by non-orthogonal multiple access (NOMA), giving priority to URLLC and resulting in eMBB quality of service (QoS) degradation. In this paper, we address the issue of joint URLLC and eMBB transmission, focusing on the problem from the perspective of delay-tolerant eMBB. Due to the priority given to URLLC, we assume that an eMBB session may be interrupted if there are no free resources available for URLLC or delayed when a new eMBB session arrives. To make the scheme more flexible, we propose that interrupted and delayed eMBB sessions periodically check for free resources, rather than continuously. To analyze this scenario, we propose a retrial queuing system with two retrial buffers (orbits) for interrupted and delayed eMBB sessions. The stationary probability distribution, provided in matrix form by recursive formulas, is presented. The paper concludes with a numerical example showing that the scheme with two buffers, compared to one buffer, practically doubles the average number of active eMBB sessions while keeping the interruption probability below 0.001. We provide an illustration of the configuration of eMBB retrial rates to meet its QoS requirements. [ABSTRACT FROM AUTHOR]

The 3GPP standardized the physical layer specification in 5G New Radio to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) coexistence in usage scenarios including aerial vehicles (AVs). Dynamic multiplexing of URLLC traffic was standardized to increase the outage capacity. DM allocates a fully overlapped bandwidth part (BWP) of eMBB and URLLC AVs to perform the immediate scheduling of URLLC traffic by puncturing ongoing eMBB traffic. However, DM often suffers from a significant frame error incurred by puncturing. Meanwhile, BWP can be sliced orthogonally for eMBB and URLLC AVs, possibly preventing overdimensioning the resources depending on the eMBB and URLLC traffic loads. In this paper, we propose a dynamic BWP allocation scheme that switches between two multiplexing methods, dynamic multiplexing (DM) and orthogonal slicing (OS), so as to minimize an impact of uRLLC traffic on eMBB traffic. To implement efficient BWP allocation, the capacity region is analyzed by considering the effect of physical layer parameters, such as modulation and coding scheme (MCS) levels and code block group size on DM and OS. OS is effective for improving the eMBB throughput under a URLLC latency constraint for deterministic and predictable URLLC traffic, whereas DM has limited error-correcting capability against the URLLC's puncturing effect. The relative MCS level of eMBB and URLLC is critical in determining the eMBB traffic tolerance against puncturing. Identifying the performance tradeoff between DM and OS, the tolerance level is quantified by a URLLC load threshold. It is given in an approximate closed form, which is an essential reference for selecting DM over OS, enabling dynamic BWP allocation for the URLLC AV. [ABSTRACT FROM AUTHOR]

Many mission-critical and latency-sensitive applications require ultra-reliable and low-latency communications (URLLC), which has been listed as a new service category of 5G New Radio (NR). To guarantee stringent latency and reliability constraints, URLLC services always exclusively occupy the spectrum and have priority over enhanced mobile broadband (eMBB) communications in the current coexistence scenario, which will greatly affect the performance of eMBB services and degrade the utilization efficiency of the spectrum resource. On the other hand, millimeter-wave (mmWave) communications can fulfill the enormous throughput requirements of 5G cellular communications. In this paper, we introduce mmWave communications into URLLC systems to provide a more efficient coexistence for eMBB and URLLC. A novel mmWave URLLC system is first developed, where URLLC users are allowed to share the spectrum resources with eMBB users. Besides, multi-connectivity technology, which enables users to access multiple base stations simultaneously, is introduced to the mmWave URLLC system to enhance the reliability. Then, a resource management problem is formulated, which maximizes the throughput of eMBB users while guaranteeing the latency and reliability requirements of URLLC users. To obtain optimal solutions, we first divide it into three subproblems, i.e., power allocation, resource matching, and user paring, and then solve them respectively. Simulation results demonstrate the data rate improvement compared against the traditional coexistence scenario without reusing strategy. Moreover, the multi-connectivity functionality poses a great effect on guaranteeing the latency and reliability requirements for URLLC users. [ABSTRACT FROM AUTHOR]

In the fifth generation (5G) era, mobile networks will provide diversified services such as enhanced Mobile BroadBand (eMBB) and ultra-reliable low-latency communication (URLLC). Furthermore, the 5G architecture will be fog-like, enabling a functional split of network functionalities between cloud and edge nodes. In this article, we propose a non-orthogonal multiple access (NOMA)-enabled fog-cloud structure in a novel density-aware fog-based radio access networks (F-RAN) to tackle different aspects of high and low-density regions, in order to meet the heterogeneous requirements of eMBB and URLLC traffic. A framework of two different problem formulations is considered to cater the high throughput and low-latency requirements in a high and low-density mode respectively. In the first problem, we study the joint caching placement and association strategy aiming at minimizing the average delay. To deal with the first problem, we apply McCormick envelopes and Lagrange partial relaxation method to transform it into three convex sub-problems, which is then solved by proposing a distributed algorithm. The second problem is to jointly optimize transmission mode selection, subchannel assignment and power allocation to maximize the sum data rate of all fog user equipments (F-UEs) while satisfying fronthaul capacity and fog-computing access point (F-AP) power constraints. Moreover, for given transmission mode selection and subchannel assignment, the optimal power allocation is derived in a closed-form expression. Simulation results are provided to validate the effectiveness of the proposed NOMA-enabled F-RAN framework and reveal that the ultra-low latency and high throughput can be achieved by properly utilizing the available resources. [ABSTRACT FROM AUTHOR]

The increase in staff shortage, particularly in urgently needed care professions, is becoming an immense problem in the German healthcare system. This critical issue is further intensified by the fact that the healthcare system is still a strongly human-dependent and often insufficiently interconnected environment. Automation, medical robotics and the concept of model-based medicine can play a key role not only in addressing these issues and supporting clinical staff, but also in enabling new applications to improve patient care. While many existing approaches aim to tackle these challenges, they are generally based on current network architectures and standards and are thus constrained by the limitations of these networks. This article highlights the need for a new network approach such as 6G, which offers flexible, dynamic structures, new communication classes and many other new features in order to leverage the true potential of these new technologies. [ABSTRACT FROM AUTHOR]

We revisit the problem of the computation of the limiting characteristics of (in)homogeneous continuous-time Markov chains with the finite state space. In general, it can be performed only numerically. The common rule of thumb is to interrupt calculations after quite some time, hoping that the values at some distant time interval will represent the sought-after solution. Convergence or ergodicity bounds, when available, can be used to answer such questions more accurately; i.e., they can indicate how to choose the position and the length of that distant time interval. The logarithmic norm method is a general technique that may allow one to obtain such bounds. Although it can handle continuous-time Markov chains with both finite and countable state spaces, its downside is the need to guess the proper similarity transformations, which may not exist. In this paper, we introduce a new technique, which broadens the scope of the logarithmic norm method. This is achieved by firstly splitting the generator of a Markov chain and then merging the convergence bounds of each block into a single bound. The proof of concept is illustrated by simple examples of the queueing theory. [ABSTRACT FROM AUTHOR]

Network slicing is crucial to the 5G architecture because it enables the virtualization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijkstra's algorithm for link mapping. During the slice creation phase, the proposed DNN training method's classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation, metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature. [ABSTRACT FROM AUTHOR]

Within the evolving landscape of fifth‐generation (5G) wireless networks, the introduction of network‐slicing protocols has become pivotal, enabling the accommodation of diverse application needs while fortifying defences against potential security breaches. This study endeavours to construct a comprehensive network‐slicing model integrated with an attack detection system within the 5G framework. Leveraging software‐defined networking (SDN) along with deep learning techniques, this approach seeks to fortify security measures while optimizing network performance. This undertaking introduces network slicing predicated on SDN with the OpenFlow protocol and Ryu control technology, complemented by a neural network model for attack detection using deep learning methodologies. Additionally, the proposed convolutional neural networks‐long short‐term memory approach demonstrates superiority over conventional ML algorithms, signifying its potential for real‐time attack detection. Evaluation of the proposed system using a 5G dataset showcases an impressive accuracy of 99%, surpassing previous studies, and affirming the efficacy of the approach. Moreover, network slicing significantly enhances quality of service by segmenting services based on bandwidth. Future research will concentrate on real‐world implementation, encompassing diverse dataset evaluations, and assessing the model's adaptability across varied scenarios. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach towards space–ground integrated communication systems by combining terrestrial cellular networks, UAV networks, and satellite networks, leveraging advanced slicing technology. The proposed architecture addresses the challenges posed by future user surges and aims to reduce network overhead effectively. Central to our approach is the introduction of a marginal mobile station (MS)-assisted network resource allocation decision architecture. Building upon this foundation, we introduce the DP-DQN model, an enhanced decision-making algorithm tailored for MSs in dynamic network environments. Furthermore, this study introduces a feedback mechanism to ensure the accuracy and adaptability of the marginalization model over time. Through extensive simulations and experimental validations, our DP-DQN-based edge decision method demonstrates substantial potential in alleviating core network overhead while improving success access ratios compared to conventional methods. [ABSTRACT FROM AUTHOR]

Rapid advancements in the development of smart terminals and infrastructure, coupled with a wide range of applications with complex requirements, are creating traffic demands that current networks may not be able to fully handle. Accordingly, the study of 6G networks deserves attention from both industry and academia. Artificial intelligence (AI) has emerged for application in the optimization and design process of new 6G networks. The developmental trend of 6G is towards effective resource management, along with the architectural improvement of the current network and hardware specifications. Cloud RAN (CRAN) is considered one of the major concepts in sixth- and fifth-generation wireless networks, being able to improve latency, capacity, and connectivity to huge numbers of devices. Besides bettering the current set-up in terms of setting the carriers' network architecture and hardware specifications, among other potential enablers, the developmental trend of 6G also means that there must be effective resource management. As a result, this study covers a thorough analysis of resource management plans in CRAN, optimization, and AI taxonomy, and how AI integration might enhance existing resource management. [ABSTRACT FROM AUTHOR]

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Network slicing empowers 5G with enhanced network performance and efficiency, cost saving, and better QoS and customer satisfaction, and expands the commercial application scenarios of 5G networks. However, the introduction of new techniques usually raises new security threats. Most of the existing works on 5G security only focus on 5G itself and do not analyze 5G network slicing security in detail. We consider network slices as a virtual logical network that can unite the subnetwork parts of 5G. If a 5G network slice has security problems or has been attacked, the entire 5G network will have security risks. In this paper, after synthesizing the existing literature, we analyze the security threats step by step through the lifecycle of 5G network slices, analyzing and summarizing more than 70 security threats in three major categories. Based on the security issues investigated, from a viewpoint of building a secure 5G network slicing system, we compiled 24 security requirements and proposed the corresponding recommendations for different scenarios of 5G network slicing. Finally, we collated the future research trends of 5G network slicing security. [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future. [ABSTRACT FROM AUTHOR]

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system's robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes. [ABSTRACT FROM AUTHOR]

5G and beyond 5G (B5G) wireless systems promise to support services with different requirements in the same network, as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communication (mMTC). One alternative is to consider the network slicing paradigm, where the wireless network resources are shared (or sliced) among active services with different requirements. In addition, another emerging technology, that is considered as a key enabler for B5G wireless systems, is the intelligent reflecting surfaces (IRS). From the deployment of an IRS, it is possible to improve the received signal quality and consequently increase the overall network capacity. Therefore, in this paper, we investigate the use of IRS to support simultaneous eMBB and URLLC services. We evaluate the achievable rate of an IRS-aided radio access network, where the uplink resources are shared between eMBB and URLLC users either under heterogeneous orthogonal multiple access (H-OMA) or heterogeneous non-orthogonal multiple access (H-NOMA) techniques. Results show that exploiting an IRS can considerably increase the eMBB rate and the URLLC reliability simultaneously, regardless of whether operating under H-OMA or H-NOMA. Moreover, we also provide some insights on the best user pairing strategy, showing that higher rates are achieved by matching many eMBB users near to the IRS with a URLLC user close to the base station.