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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]

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity. To overcome this issue, optimal and low complexity power allocation schemes are derived. In the simulation results, the performance of the proposed CoMP-assisted NOMA and CoMP-assisted C-NOMA schemes are compared with those of the CoMP-assisted orthogonal-multiple-access (OMA) scheme, the C-NOMA scheme and the NOMA scheme, where the superiority of the proposed schemes are demonstrated. Finally, the performance of the proposed schemes and the considered baselines is evaluated while varying various system parameters. [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 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]

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]

Modern 5G services with stringent reliability and latency requirements such as smart healthcare and industrial automation have become possible through the advancement of Multi-access Edge Computing (MEC). However, the rigidity of ground MEC and its susceptibility to infrastructure failure would prevent satisfying the resiliency and strict requirements of those services. Unmanned Aerial Vehicles (UAVs) have been proposed for providing flexible edge computing capability through UAV-mounted cloudlets, harnessing their advantages such as mobility, low-cost, and line-of-sight communication. However, UAV-mounted cloudlets may have failure rates that would impact mission-critical applications, necessitating a novel study for the provisioned reliability considering UAV node reliability and task redundancy. In this paper, we investigate the novel problem of UAV-aided ultra-reliable low-latency computation offloading which would enable future IoT services with strict requirements. We aim at maximizing the rate of served requests, by optimizing the UAVs’ positions, the offloading decisions, and the allocated resources while respecting the stringent latency and reliability requirements. To do so, the problem is divided into two phases, the first being a planning problem to optimize the placement of UAVs and the second an operational problem to make optimized offloading and resource allocation decisions with constrained UAVs’ energy. We formulate both problems associated with each phase as non-convex mixed-integer programs, and due to their non-convexity, we propose a two-stage approximate algorithm where the two problems are transformed into approximate convex programs. Further, we approach the problem considering the task partitioning model which will be prevalent in 5G networks. Through numerical analysis, we demonstrate the efficiency of our solution considering various scenarios, and compare it to other baseline approaches. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are gaining growing interests due to the paramount roles they play, particularly these days, in enabling new services that help modernize our transportation, supply chain, search and rescue, among others. They are capable of positively influencing wireless systems through enabling and fostering emerging technologies such as autonomous driving, vertical industries, virtual reality and so many others. The Internet of Vehicles is a prime sector benefiting from the services offered by future cellular systems in general and UAVs in particular, and this paper considers the problem of content delivery to vehicles on road segments with either overloaded or no available communication infrastructure. Incoming vehicles demand service from a library of contents that is partially cached at the UAV; the content of the library is also assumed to change as new vehicles carrying more popular contents arrive. Each inbound vehicle makes a request and the UAV decides on its best trajectory to provide service while maximizing a certain operational utility. Given the energy limitation at the UAV, we seek an energy efficient solution. Hence, our problem consists of jointly finding caching decisions, UAV trajectory and radio resource allocation which is formulated mathematically as a Mixed Integer Non-Linear Problem (MINLP). However, owing to uncertainties in the environment (e.g., random arrival of vehicles, their requests for contents and their existing contents), it is often hard and impractical to solve using standard optimization techniques. To this end, we formulate our problem as a Markov Decision Process (MDP) and we resort to tools such as Proximal Policy Optimization (PPO), a very promising Reinforcement Learning method, along with a set of crafted algorithms to solve our problem. Finally, we conduct simulation-based experiments to analyze and demonstrate the superiority of our solution approach compared with four counterparts and baseline schemes. [ABSTRACT FROM AUTHOR]

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV’s flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics. [ABSTRACT FROM AUTHOR]

Heterogeneous networks have allowed network operators to enhance the spectral efficiency and support large number of devices by deploying close small-cells. Recently, Multi-access Edge Computing (MEC) has become an enabler for modern latency-sensitive 5G services by pushing tasks computation to the network edge. In this paper, we study the problem of task offloading in a MEC-enabled heterogeneous network with low-cost wireless backhaul, where we minimize the total devices’ energy consumption while respecting their latency deadline. We explore the benefit of leveraging the macro-cell cloudlet for computing small-cell users’ tasks, where the allocation of backhaul radio resources is optimized. We also jointly optimize the partial offloading decision, transmit power, and the allocation of access radio and computational resources. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method that provides an approximate solution. Through numerical analysis, we perform simulations based on varying configurations, and demonstrate the performance and efficiency of our proposed solution. [ABSTRACT FROM AUTHOR]

The proliferation of smart connected Internet of Things (IoT) devices is bringing tremendous challenges in meeting the performance requirement of their supported real-time applications due to their limited resources in terms of computing, storage, and battery life. In addition, the considerable amount of data they generate brings extra burden to the existing wireless network infrastructure. By enabling distributed computing and storage capabilities at the edge of the network, multi-access edge computing (MEC) serves delay sensitive, computationally intensive applications. Managing the heterogeneity of the workload generated by IoT devices, especially in terms of computing and delay requirements, while being cognizant of the cost to network operators, requires an efficient dimensioning of the MEC-enabled network infrastructure. Hence, in this paper, we study and formulate the problem of MEC resource provisioning and workload assignment for IoT services (RPWA) as a mixed integer program to jointly decide on the number and the location of edge servers and applications to deploy, in addition to the workload assignment. Given its complexity, we propose a decomposition approach to solve it which consists of decomposing RPWA into the delay aware load assignment sub-problem and the mobile edge servers dimensioning sub-problem. We analyze the effectiveness of the proposed algorithm through extensive simulations and highlight valuable performance trends and trade-offs as a function of various system parameters. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) has formed a major improvement in the existing mobile cloud computing paradigm, due to its ability in addressing the rising number of latency-sensitive services. However, bearing in mind the limited capacity that edge servers possess which offsets their benefits in the periods of high load, a hierarchical arrangement of the edge cloudlets has been studied and has shown to be successful in expanding their capabilities. Yet, considering the emerging business models in 5G networks, the cost disparity between the edge tiers has been until now ignored, leading to cost-inefficient solutions with respect to the network operators (NOs). In this paper, we consider an NO that is leasing resources of a high-tier central cloudlet for task offload, where we jointly minimize the NO’s computational cost and devices’ energy consumption in a multi-tier MEC system, by optimizing the offloading decision, the allocated transmission power and radio resources on the uplink channel, and the assigned servers’ computation, while respecting the devices’ latency requirement. We mathematically formulate our mixed-integer non-convex program and propose a Branch-and-Bound (BnB) algorithm for obtaining the optimal solution. Due to the BnB complexity, we propose a low-complexity algorithm based on the successive convex approximation method to solve and obtain a high-quality solution and also present an inflation-based algorithm for obtaining a polynomial-time and efficient solution. The numerical results show the performance and scalability of the algorithms, demonstrate their efficiency, and uncover insights for helping NOs to better manage their resources following various configurations. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) has recently emerged as a novel paradigm to facilitate access to advanced computing capabilities at the edge of the network, in close proximity to end devices, thereby enabling a rich variety of latency sensitive services demanded by various emerging industry verticals. Internet-of-Things (IoT) devices, being highly ubiquitous and connected, can offload their computational tasks to be processed by applications hosted on the MEC servers due to their limited battery, computing, and storage capacities. Such IoT applications providing services to offloaded tasks of IoT devices are hosted on edge servers with limited computing capabilities. Given the heterogeneity in the requirements of the offloaded tasks (different computing requirements, latency, and so on) and limited MEC capabilities, we jointly decide on the task offloading (tasks to application assignment) and scheduling (order of executing them), which yields a challenging problem of combinatorial nature. Furthermore, we jointly decide on the computing resource allocation for the hosted applications, and we refer this problem as the Dynamic Task Offloading and Scheduling problem, encompassing the three subproblems mentioned earlier. We mathematically formulate this problem, and owing to its complexity, we design a novel thoughtful decomposition based on the technique of the Logic-Based Benders Decomposition. This technique solves a relaxed master, with fewer constraints, and a subproblem, whose resolution allows the generation of cuts which will, iteratively, guide the master to tighten its search space. Ultimately, both the master and the sub-problem will converge to yield the optimal solution. We show that this technique offers several order of magnitude (more than 140 times) improvements in the run time for the studied instances. One other advantage of this method is its capability of providing solutions with performance guarantees. Finally, we use this method to highlight the insightful performance trends for different vertical industries as a function of multiple system parameters with a focus on the delay-sensitive use cases. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the downlink transmissions in wireless backhaul (WB) networks when unmanned aerial vehicles (UAVs) are used as flying small cell base stations. We propose to employ the non-orthogonal multiple access (NOMA) on the WB transmissions and introduce a novel cooperative transmission scheme for the wireless access links. Then, we formulate an optimization problem which jointly determines the radio resource allocation at the macro cell base station (MBS) and UAVs along with the optimization of the decoding order of the NOMA process and the positions of the UAVs in space to maximize the sum achievable rate of all users. The formulated problem is a general mixed integer non-convex program, which is very difficult to solve optimally within a polynomial time. Therefore, we propose a framework based on the method of difference of convex program characterized by the Lipschitz continuity to transform and approximate the original problem into a series of convex approximate ones and develop a low-complexity algorithm to sequentially solve for each approximate problem until convergence. Numerical evaluation and analysis show that our achieved solution, under the proposed framework and developed algorithm, can outperform the other schemes which aim at either optimizing without using cooperative NOMA or do not optimize the UAVs’ positions. [ABSTRACT FROM AUTHOR]

In this paper, we consider a novel model of two-tier wireless backhaul (WB) small cell networks where each small cell access point is equipped with a buffer of finite storage. We employ a time-spectrum transmission accommodation scheme to separate the WB and access transmissions on top of developing a joint optimized transmit beamforming and power allocation algorithm. Unlike previous work, we propose a more advanced buffering protocol to study the small cell performance improvement by solving the offline and online optimization problems that maximize the total small cell access rate. Both kind of problems are generally non-convex and NP-hard. The offline scheme assumes the availability of channel state information (CSI) in current and future time slots and is used as a benchmark for online algorithms. To solve it, we develop a low-complexity algorithm which iteratively solves a sequence of lower bounded convex approximated problems until convergence. The formulated online problems must be solved in a slot-by-slot manner since transmitters only know the CSI of the current time slot. Then, we also develop low-complexity online algorithms to jointly design the transmit beamforming and power allocation. Our theoretical and numerical simulation results show that our proposed model with advanced buffering strategy outperforms the conventional designs in terms of small cell access rate for both offline and online algorithms. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has been proposed as an alternative to orthogonal multiple access (OMA) in an effort to enhance the spectral efficiency of 5G cellular systems. However, the NOMA throughput gain relative to that of OMA has been shown to be modest. In this paper, we propose a novel NOMA scheme that exploits the partial overlap (i.e., similarity) among users bit sequences at the base station (BS). Specifically, users bit sequences are divided into blocks of short lengths, i.e., short bit sequences. Then, one user is selected and served during a given transmission time interval (TTI). Users whose bit sequences partially overlap with the bit sequence of the served user are also (partially) served during the same TTI. At the receiving end, the receiver corresponding to the served user recovers its entire bit sequence, whereas the partially served users recover their corresponding overlapping bit blocks and ignore the rest of the sequence. The performance of the proposed scheme is analyzed in terms of the overall throughput. We show that a throughput gain of up to three times that of existing OMA schemes can be achieved. Moreover, we show that the average rate per user decreases slightly as the number of users increases, whereas it linearly decreases with the number of users in existing NOMA schemes. We stress here that the proposed scheme completely differs from existing NOMA schemes as the latter schemes are based on power allocation at the BS where successive interference cancellation is normally used. The implication of this is that the proposed scheme provides substantial throughput gains without causing interference among users and without adopting a specific power allocation at the BS. [ABSTRACT FROM AUTHOR]

In this paper, we propose to re-engineer the wireless backhaul two-tier heterogeneous networks architecture by developing a novel cooperative transmission scheme based on non-orthogonal multiple access (NOMA). To effectively manage severe interference from the newly introduced backhaul communications, we employ the cochannel time division duplexing combined with spectrum partitioning between two considered tiers. This paper’s novelty lies in the formulation to solve for the NOMA decoding order, which affects the rule of the cooperation between small cell transmissions. We propose two optimization problems of jointly designing the NOMA decoding order together with the transmit beamforming at the macro base station and power allocation at the small cells which maximize the total achievable rate and the number of satisfied users, respectively. The first and second formulated problems are both mixed integer non-convex and are generally NP-hard. To solve them, we first employ the difference of convex functions to present the formulated binary variables and then equivalently transform the optimization problems into more tractable forms. Finally, we develop an iterative low-complexity algorithm based on successive convex approximation technique and majorization minimization method, which is provable to eventually converge at a sub-optimal solution. Numerical results are extensively studied to corroborate that our proposed strategy outperforms the conventional designs in terms of total achievable rate and number of satisfied users. [ABSTRACT FROM AUTHOR]

This paper studies the joint design of downlink transmit beamforming and power allocation in two-tier wireless backhaul small cell heterogeneous networks. We consider reverse time division duplexing combined with equal spectrum splitting between two tiers for interference mitigation. We formulate a constrained optimization problem with the objective of maximizing the proposed access energy efficiency, defined by the ratio of the sum achievable rate at the users to the overall consumed power, where the power consumption model includes the adaptive decoding power. The formulated problem is non-convex and generally NP-hard. To solve it, we first apply the high-complexity branch-and-bound algorithm to find the global optimal solution. Then, we develop a lower complexity algorithm which iteratively solves the convex approximated problem until convergence. Compared with the conventional methods, this algorithm converges faster to a solution that is very close to the global optimal solution achieved by the branch-and-bound approach. Finally, we exploit the framework of the alternating direction method of multipliers on the convex approximated problem to develop a distributed algorithm. Numerical results are obtained to show the improvement of our proposed model with much better power conservation compared with the different design of fixed circuit power assignment. [ABSTRACT FROM PUBLISHER]

This paper studies two-tier heterogeneous cellular networks, with wireless backhaul communication, consisting of macrocell and small cell tiers. A joint design of transmit beamforming, power allocation, and bandwidth partitioning for both uplink and downlink transmissions is considered. By assuming the reverse time division duplexing system, we propose a strategy to partition the bandwidth for two consecutive time slots by two separate partitioning factors. Under the proposed strategy, we formulate a constrained optimization problem with the objective of maximizing the sum rate of small cell uplink and downlink. For this non-convex problem, we leverage the sequential parametric convex approximation method to find the stationary point of the problem. In this method, a convex approximation of the problem is solved at each iteration. Furthermore, with appropriate transformations, we approximate the problem as second-order cone programming (SOCP) and propose a fast converging algorithm to attain the solution. We also evaluate the impact of imperfect channel state information by reformulating the optimization problem and applying the proposed algorithm to solve it. We conduct numerical simulations to show that the joint design of transmit beamforming, power allocation, and bandwidth partitioning leads to a better resource utilization and high spectral efficiency. Moreover, our results show that the proposed SOCP-based algorithm converges fast to a solution, which is shown to be closer to the global optimal solution achieved by the branch-and-bound algorithm compared with other works. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel framework for managing the resource provisioning of reliable virtual networks (VN) in the cloud. This includes handling the placement of VN requests while providing availability guarantees, as well as reconfiguring/adapting their placement as their request changes over time. This is particularly interesting for services with periodic resource demands. Given the heterogeneous failure rates of physical network components, the placement and reconfiguration must ensure that the selected hosts for each VN meets its availability requirements. The existing work on availability-aware VN placement has overlooked the case of “availability over-provisioning,” as well as the fact that VN requests are subject to change over time. To this extent, we propose a novel framework that consists of two main modules; JENA: a tabu-based availability-aware resource allocation (embedding) module for VNs that achieves “just-enough” availability guarantees, and ARES: a reliable reconfiguration module to adapt the embedding of hosted services as they scale. Further, we introduce the concept of “protection-domains” and “protection-policies” to equip our proposed modules with the ability to augment services with redundant/backup nodes to enhance their reliability. Our numerical results show that our framework enhances network’s admissibility (with 33% lower blocking compared to existing work), and in return increases the cloud provider’s long term revenue, compared to peer and benchmark algorithms. [ABSTRACT FROM PUBLISHER]

Network virtualization is a key provision for improving the scalability and reliability of cloud computing services. In recent years, various mapping schemes have been developed to reserve VN resources over substrate networks. However, many cloud providers are very concerned about improving service reliability under catastrophic disaster conditions yielding multiple system failures. To address this challenge, this work presents a novel failure region-disjoint VN mapping scheme to improve VN mapping survivability. The problem is first formulated as a mixed integer linear programming problem and then two heuristic solutions are proposed to compute a pair of failure region-disjoint VN mappings. The solution also takes into account mapping costs and load balancing concerns to help improve resource efficiencies. The schemes are then analyzed in detail for a variety of networks and their overall performances compared to some existing survivable VN mapping schemes. [ABSTRACT FROM AUTHOR]

This paper addresses, in the context of wireless network design, the problem of optimally partitioning the spectrum into a set of nonoverlapping channels with nonuniform spectrum widths. While narrower bands split the total available spectrum into more nonoverlapping channels allowing more parallel concurrent transmissions, wider spectrum bands yield links with larger transport capacity. Thus, we model the combinatorially complex problem of joint routing, link scheduling, and variable-width channel allocation in both single and multirate multihop wireless networks as a mixed integer linear program, and present a solution framework using the column generation decomposition approach. Given the nature and complexity of the resulting dual subproblem, we propose heuristic methods for partitioning the spectrum and allocating resources to each active links, and hence obtain solutions for larger network instances. We present several numerical results and engineering insights suggesting both spectrum width and transmission rates as effective tunable knobs for combatting interference and promoting spatial reuse and thus achieving superior performance in multihop settings. [ABSTRACT FROM AUTHOR]

Ethernet Ring Protection (ERP) has recently emerged to provide protection switching for Ethernet ring topologies with sub-50 ms failover capabilities. In addition to Ethernet's cost-effectiveness and simplicity, ERP's promise to also provide protection in mesh packet transport networks positions Ethernet as a prominent competitor to conventional SONET/SDH and the technology of choice for carrier networks. Higher service availability, however, in ERP mesh networks has been challenged by the issue of network partitioning and the contention for protection resources which may be caused by concurrent failures. In this paper, we show that in a mesh network designed to withstand only single failure situations, network services usually suffer from two outage categories subject to concurrent dual-link failures. We address the problem of minimal capacity network design to provide high service availability against concurrent dual-link failures. We cast this combinatorially complex design problem as an optimization one and show that higher service availability can be achieved by proper RPL (Ring Protection Link) placement and ring hierarchy selection. The objective is to maximize the network flow under any dual-link failure scenario. Our design achieves minimal capacity allocation that minimizes the number of service outages (up to 37%) therefore achieving higher service availability. Numerical evaluation and comparative study show that the joint desgin approach of the ILP model provisions 8% less capacity than the sequential two-step approach to achieve similar service availability. [ABSTRACT FROM AUTHOR]

The recent Ethernet Ring Protection (ERP) method brings new challenges into the design of next generation Ethernet carrier networks. This letter addresses one of the design challenges that may confront a network operator in its migration to provide ERP capabilities over an existing network. The objective of the design is to properly allocate network resources to maximize the service provider's revenue given the per-link deployed capacity. We formulate this problem of resource allocation as an optimization model with the objective of maximizing the network flow using the well-known max-min approach. Our proposed model ensures fairness among the admissible flows and the design of a proper protection plan to survive against any single link failure. Numerical results show that the proposed design approach can increase the overall network flow by up to 44.7% in comparison to an arbitrarily designed ERP network while it allocates bandwidth 29.3% more fairly among the sessions. [ABSTRACT FROM PUBLISHER]

We investigate the joint problem of relay selection and optimal sharing of relay power in wireless cellular networks with multicast traffic. We first present a mixed Boolean-convex optimization model and solve this combinatorial problem optimally using branch and bound technique. We then show that obtaining the optimal solution is computationally infeasible for large network sizes and, unlike the case of unicast traffic, a water filling method does not yield near optimal solutions in multicast scenarios. We thus adopt an algorithm based on sequential fixing which substantially reduces the computation time and achieves near optimal solutions. [ABSTRACT FROM PUBLISHER]

Network survivability is a key design issue for optical transport mesh networks. Various survivability schemes have been introduced among which p-cycle has (and continues) attracted quite a lot of attention because of its fast and efficient protection capabilities. The concept of p-cycle has been generalized to pre-cross-connected trails, or p-trails, by exploiting the fact that providing pre-cross-connected protection paths and obtaining fast restoration do not necessarily require a cyclic structure as in p-cycles. In this paper, we investigate the benefits and sharing capabilities of p-trails and observe that non-simple p-trails and p-cycles can be built from merging simple trails. We derive two ILP models for survivable network design using p-trails. Our first design model is a simple ILP whose optimal solution relies on the exhaustive enumeration of all simple trails in the network. We observe that the size of our ILP model, and therefore the computation time, become prohibitively large making the model unpractical for larger network instances. Therefore, to overcome this scalability issue, we develop an enhanced model for this complex optimization problem using the column generation (CG) decomposition technique. Our developed design approach is shown to be very scalable, as opposed to other prior p-trail design methods; further, we show that p-trails are more efficient than p-cycles in terms of protection resource redundancy in the network. [ABSTRACT FROM AUTHOR]

We investigate the problem of flexible spectrum access in multihop wireless networks. We assume radios that are capable of transmitting on channels of contiguous frequency bands and which do not require any sophisticated processing. Because these radios can flexibly configure their transmissions anywhere in the available frequency band, the spectrum becomes vulnerable to fragmentation and interference. We consider the joint problem of routing, link scheduling and spectrum allocation where scheduling feasibility is considered under the physical interference (SINR) constraint. We present a primal-dual decomposition for this complex optimization problem based on column generation. We show that obtaining the optimal solution to this problem is computationally not feasible, except for very small networks. We thus adopt a two-fold method to circumvent the complexity while yielding practical solutions. First, we relax the SINR constraint and use a simplified graph-based model for the interference. Second, we use a simulated annealing (SA) approach to solve the dual subproblem. Our SA approach however is augmented with an SINR feasibility check. Our results confirm that the primal-dual decomposition method using SA substantially reduces the computation time and achieves near optimal solutions. The results also reveal that substantial improvement in network performance is obtained with flexible spectrum assignment which results from its capability of better managing the interference in the network. [ABSTRACT FROM PUBLISHER]