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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]

The gaining momentum of Electric Vehicles’ (EV) market is hindered mainly due to the range anxiety. Accordingly, a ubiquitous charging station (CS) network is becoming indispensable. However, due to the lack of reservations or check-in policies in EV charging, users and operators are not provided proper information regarding waiting time at public CSs. This renders users reluctant to use public CSs. In addition, this incomplete information, creates difficulties in the deployment and operation of CSs. Evidently, there is a need to improve the Quality-of-Service (QoS) such as minimizing the waiting time or blocking probability. Therefore, CS owners rely during the designing stage on some theoretical distribution for the associated parameters assumption (i.e., battery capacity, charging demand, charging time, waiting time, etc.). To alleviate this situation, instead of depending on theoretical assumptions, real CSs usage data for EV charging are analyzed to acquire data driven distributions. Moreover, since the charging rate is dependent on the State of Charge (SoC), instead of a constant charging rate, a SoC dependent charging model based on real experimental data is proposed and evaluated with real data. Finally, exploiting the acquired distributions and charging model, variations of the $M/G/k$ queuing system to approximate the waiting time, reneging probability and blocking probability is implemented. A detailed simulation is placed and the findings provide a direction for CS owners in determining the capacity (i.e., number of outlets) or parking area size to enhance the QoS. [ABSTRACT FROM AUTHOR]

This paper investigates the downlink transmission of reconfigurable intelligent surface (RIS)-aided cooperative non-orthogonal-multiple-access (C-NOMA), where both half-duplex (HD) and full-duplex (FD) relaying modes are considered. The system model consists of one base station (BS), two users and one RIS. The goal is to minimize the total transmit power at both the BS and at the user-cooperating relay for each relaying mode by jointly optimizing the power allocation coefficients at the BS, the transmit power coefficient at the relay user, and the passive beamforming at the RIS, subject to power budget constraints, the successive interference cancellation constraint and the minimum required quality-of-service at both cellular users. To address the high-coupled optimization variables, an efficient algorithm is proposed by invoking an alternating optimization approach that decomposes the original problem into a power allocation sub-problem and a passive beamforming sub-problem, which are solved alternately. For the power allocation sub-problem, the optimal closed-form expressions for the power allocation coefficients are derived. Meanwhile, with the aid of difference-of-convex rank-one representation and successive convex approximation, an efficient solution for the passive beamforming is obtained. The simulation results validate the accuracy of the derived power control closed-form expressions and demonstrate the gain in the total transmit power brought by integrating the RIS in C-NOMA networks. [ABSTRACT FROM AUTHOR]

We consider a wireless network consisting of two adjacent cells, where the joint transmission (JT) coordinated multipoint (CoMP) is established to assist the user equipments (UEs) located at the edge of each cell. In addition, full-duplex (FD) cooperative non-orthogonal-multiple-access (C-NOMA) is invoked within each cell to improve the data rates of the UEs and to assist those at the cell edge. The UEs are categorized into two groups, namely, cell-center UEs ($CUs$) and cell-edge UEs ($EUs$). The $CUs$ are the UEs located around the center of each cell. Meanwhile, the $EUs$ are the UEs located at the edge of each cell, where the JT-CoMP is applied since they have less distinctive received power from two cells. In this paper, a framework to jointly optimize the power control and the UEs clustering of CoMP-assisted FD C-NOMA system is formulated as an optimization problem to maximize the network sum-rate while guaranteeing the required quality-of-service of UEs. The formulated problem is a non-convex mixed-integer non-linear program that cannot be solved in a straightforward manner. To tackle this issue, the formulated problem is decomposed into an inner power allocation problem and an outer UEs clustering problem. For the inner problem, a computational-efficient solution is obtained. Meanwhile, the outer problem is reformulated as a one-to-one three-sided matching game. Then, a low-complexity near-optimal clustering algorithm is proposed. The simulation results demonstrate that 1) the optimality of the power control solution; 2) the CoMP-assisted FD C-NOMA has a superior performance compared to CoMP-assisted half-duplex (HD) C-NOMA and CoMP NOMA schemes for moderate values of self-interference. It has been also shown that the proposed solution achieves around 96.5% of the average achievable network sum-rate of the optimal solution. [ABSTRACT FROM AUTHOR]

This paper investigates the integration of reconfigurable intelligent surface (RIS) with downlink non-orthogonal-multiple-access (NOMA) in a multi-user two-cell network assisted by the joint-transmission coordinated multipoint (JT-CoMP). Specifically, the RIS is deployed at the edge of two adjacent cells to assist the JT-CoMP from these two cells to multiple far NOMA users located at their edges. Under this setup, we jointly optimize the power allocation (PA) coefficients at the base stations (BSs), the user clustering (UC) policy, and the phase-shift (PS) matrix of the RIS with the objective of maximizing the network sum-rate subject to a target quality-of-service, defined in terms of the minimum required data rate at each cellular user, and the successive interference cancellation (SIC) constraints. The formulated problem ends to be a non-convex mixed-integer non-linear program that is difficult to be solved in a straightforward manner. To alleviate this issue, and with the aid of alternating optimization (AO), the original optimization problem is decomposed into two sub-problems, a joint PA and UC sub-problem and a PS sub-problem, that are solved in an alternating way. For the first sub-problem, we invoke the bi-level optimization approach to decouple the PA sub-problem from the UC sub-problem. For the PA sub-problem, closed-form expressions for the optimal PA coefficients are derived. On the other hand, the UC problem is projected to multiple 2-dimensional assignment problems, each of which is solved using the Hungarian method. Finally, the PS sub-problem is formulated as a difference-of-convex problem and an efficient solution is obtained using the successive convex approximation technique. The numerical results reveal that the network sum-rate of the proposed RIS-assisted CoMP NOMA networks outperforms the conventional CoMP NOMA scheme without the assistance of the RIS, the RIS-assisted CoMP orthogonal multiple access (OMA) scheme, and RIS-assisted NOMA scheme, especially for low transmit power from the BSs. [ABSTRACT FROM AUTHOR]

Wireless access protocols currently deployed in mobile ad hoc networks use distributed contention resolution mechanisms for sharing the wireless channel. In such an environment, selfish hosts that fail to adhere to the medium access control (MAC) protocol may obtain an unfair share of the channel bandwidth at the expense of performance degradation of well-behaved hosts. We present a novel access method, called predictable random backoff (PRB), that is capable of mitigating the misbehavior of selfish hosts, particularly hosts that deliberately do not respect the random deferment of the transmission of their packets. PRB is based on minor modifications of the IEEE 802.11 binary exponential backoff (BEB) and forces each node to generate a predictable backoff interval. The key idea is to adjust, in a predictable manner, the lower bound of the contention window to enhance the per-station fairness in selfish environments. Hosts that do not follow the operation of PRB are therefore easily detected and isolated. We present an accurate analytical model to compute the system throughput using a 3-D Markov chain. We evaluate the performance of PRB under the normal case and in the presence of selfish hosts. Our results show that PRB and BEB similarly perform in the former case. Selfish hosts, however, achieve substantially higher throughput than well-behaved hosts under BEB. PRB, on the other hand, can effectively enhance IEEE 802.11 BEB by mitigating the impacts of these MAC selfish misbehaviors and guarantee a fair share of the wireless channel for well-behaved hosts. Index Terms--Ad hoc networks, IEEE 802.11, medium access control (MAC), performance evaluation, selfish misbehavior.

The emerging IEEE 802.11e standard is expected to provide service differentiation and resource allocation for realtime traffic. To support the transmission of voice and multimedia data with performance guarantees, it is crucial to design efficient algorithms for admission control and resource allocation (particularly under heavy load), and several methods have been proposed to date. However, most of these proposed methods may not be efficient, because they assign channel access parameters (CAPs) according to the access category into which a flow is mapped rather than the absolute quality-of-service (QoS) requirements of the flow. Using simulations, we highlight the shortcomings of current admission control methods, and accordingly, we propose a flow-based service differentiation mechanism. We introduce the proper 'per-flow' selection of CAPs based on the QoS requirements of traffic flows. Furthermore, the new flow-based admission can control and adjust CAPs with the channel condition variations. We validate our reasoning through extensive simulations and a thorough comparison with other schemes. Index Terms--Admission control, IEEE 802.11e, wireless networks.

Given its ability to control and manipulate wireless environments, reconfigurable intelligent surface (RIS), also known as intelligent reflecting surface (IRS), has emerged as a key enabler technology for the six-generation (6G) cellular networks. In the meantime, vehicular environment radio propagation is negatively influenced by a large set of objects that cause transmission distortion such as high buildings. Therefore, this work is devoted to explore the area of RIS technology integration with vehicular communications while considering the dynamic nature of such communication environment. Specifically, we provide a system model where RoadSide Unit (RSU) leverages RIS to provide indirect wireless transmissions to disconnected areas, known as dark zones. Dark zones are spots within RSU coverage where the communication links are blocked due to the existence of blockages. In details, a discrete RIS is utilized to provide communication links between the RSU and the vehicles passing through out-of-service zones. Therefore, the joint problem of RSU resource scheduling and RIS passive beamforming or phase-shift matrix is formulated as an optimization problem with the objective of maximizing the minimum average bit rate. The formulated problem is mixed integer non-convex program which is difficult to be solved and does not account for the uncertain dynamic environment in vehicular networks. Thereby, we resort to alternative methods based on Deep Reinforcement Learning to determine RSU wireless scheduling and Block Coordinate Descent (BCD) to solve for the phase-shift matrix, i.e., passive beamforming, of the RIS. The Markov Decision Process (MDP) is defined and the complexity of the solution approach is discussed. Our numerical results demonstrate the superiority of our proposed approach over baseline techniques. [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]

Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. In an effort to satisfy the driver’s well-being and demand for continuous connectivity in the IoV era, this paper addresses both safety and quality-of-service (QoS) concerns in a green, balanced, connected, and efficient vehicular network. Using the recent advances in training deep neural networks, we exploit the deep reinforcement learning model, namely deep Q-network, which learns a scheduling policy from high-dimensional inputs corresponding to the current characteristics of the underlying model. The realized policy serves to extend the lifetime of the battery-powered vehicular network while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform several scheduling benchmarks in terms of completed request percentage (10–25%), mean request delay (10–15%), and total network lifetime (5–65%). [ABSTRACT FROM AUTHOR]

In a vehicular network where roadside units (RSUs) are deprived from a permanent grid-power connection, vehicle-to-infrastructure (V2I) communications are disrupted once the RSU's battery is completely drained. These batteries are recharged regularly either by human intervention or using energy harvesting techniques, such as solar or wind energy. As such, it becomes particularly crucial to conserve battery power until the next recharge cycle in order to maintain network operation and connectivity. This paper examines a vehicular network whose RSU dispossesses a permanent power source but is instead equipped with a large battery, which is periodically recharged. In what follows, a reinforcement learning technique, i.e., protocol for energy-efficient adaptive scheduling using reinforcement learning (PEARL), is proposed for the purpose of optimizing the RSU's downlink traffic scheduling during a discharge period. PEARL's objective is to equip the RSU with the required artificial intelligence to realize and, hence, exploit an optimal scheduling policy that will guarantee the operation of the vehicular network during the discharge cycle while fulfilling the largest number of service requests. The simulation input parameters were chosen in a way that guarantees the convergence of PEARL, whose exploitation showed better results when compared with three heuristic benchmark scheduling algorithms in terms of a vehicle's quality of experience and the RSU's throughput. For instance, the deployment of the well-trained PEARL agent resulted in at least 50% improved performance over the best heuristic algorithm in terms of the percentage of vehicles departing with incomplete service requests. [ABSTRACT FROM AUTHOR]

Real-time interactive application workloads (e.g., Web search, social networking, and so on) appear in the form of a large number of mini requests and responses flowing over the datacenters’ networks. They end up being sewed all together to constitute a user-requested task or computation (e.g., display a complete Facebook timeline). Applications as such strictly impose low latency flow completion, since the service’s quality is decreed by quick aggregation of responses to the largest possible fraction of requests and their delivery back to the user. This paper presents a deadline-aware flow scheduling (DAFS). In addition to reducing the average flow completion time (FCT), DAFS aims at decreasing the deadline mismatch and blocking probabilities, hence improving the average application throughput. An analytical queuing model is formulated herein to capture the datacenter’s network dynamics and evaluate its performance when operating under DAFS. The model is validated through extensive simulations whose results also show that DAFS outperforms existing multi-queue-based priority mechanisms by 52% in terms of the average FCT and a range of 7%–29% in terms of the average throughput. [ABSTRACT FROM PUBLISHER]

This paper deals with the multiple link/node substrate failures that impact a multicast virtual network (MVN) in which link recovery is not feasible and node migration is mandatory. A novel restoration approach is introduced to repair the failed MVNs while maintaining their quality of service requirements (e.g., end-to-end delay and delay variations). This approach relies on reducing the search region and exploiting nodes ranking and filtering (NRF) techniques to speed up the recovery process of finding an alternative node to which to migrate. The performance is extensively evaluated against multiple failures, with and without NRF, compared with complete re-embedding technique, link failure algorithms for single link failure, and previous work for single node failure. Simulation results prove that our recovery technique achieves good restoration ratio in considerably fast execution time, low link mapping cost (gain) with a slight impact on the admission ratio. [ABSTRACT FROM PUBLISHER]

The failure-prone nature of data center networks has evoked countless contributions to develop proactive and reactive countermeasures. Yet, most of these techniques were developed with unicast services in mind. When in fact, multiple services hosted in data center networks today rely on multicast communication to disseminate traffic. Hence, the existing survivability schemes fail to cater to the distinctive properties and quality of service requirements that multicast services entail. This letter is devoted to understanding the ramifications of facility node or substrate link failure on multicast services residing in cloud networks. We formally define the multicast virtual network restoration problem and prove its NP-complete nature in arbitrary graphs. Furthermore, we prove that the problem can be solved in polynomial-time in multi-rooted treelike data center network topologies. [ABSTRACT FROM PUBLISHER]

Ethernet Ring Protection (ERP) switching has emerged to provide sub-50 ms of restoration times, allowing Ethernet technologies to expand beyond enterprises to next generation metro and backbone networks, providing much needed services to interconnect for instance dispersed and high-bandwidth data centers. This paper considers the problem of efficiently designing and planning an Ethernet-based metro network with ERP protection method. While previous recent work has addressed such design problem, none has considered the capabilities of exploiting multiple ERP instances, leaving behind some advantages that network providers could tap into to provide their customers with desirable quality of service support. Resource planning in ERP-based Ethernet network is, however, a complex problem due to the challenges associated with the logical link block selection as well as ring hierarchy selection. ERP instances add, however, another dimension of combinatorial complexity, making the design problem completely intractable. To address this issue, we resort to large scale optimization tools and present a novel primal-dual decomposition of the original problem using column generation. We show that our method is very scalable and obtain several design insights on various representative network instances. [ABSTRACT FROM PUBLISHER]

Abstract: Wireless LANs (WLANs) suffer degrading performance when operating within domestic areas due to multiple reasons such as: multi-path, fading, path loss and user mobility. To overcome this, transmission rate is usually adjusted to a more error-resistant rate. In this paper, we first present a novel rate adaptation algorithm for IEEE 802.11 that can efficiently identify the threshold frame error rate (FER) at which link adjustment is required, based on a simple throughput analysis at the MAC layer. Then, we extend our rate adaptation algorithm to support IEEE 802.11e quality of service (QoS) requirements. When a real-time stream with QoS requirements is admitted, critical constraints such as delay bound and maximum packet drop count are integrated in the selection of the most convenient transmission rate that best respects the flow requirements. Moreover, we use dynamic bandwidth allocation rather than the default transmission opportunities (TXOPs) in a way that best offers a flow the required time for retransmissions due to packet failure based on the variant loss rate present in the channel. We validate our proposed rate adaptation algorithms via simulation results where the efficiency and effectiveness of the algorithm are noticed for both best effort and QoS flows. [Copyright &y& Elsevier]