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Resources allocation (RA) is a challenging task in many fields and applications including communications and computer networks. The conventional solutions of such problems usually come with a time and memory cost, especially for massive networks such as Internet-of-Things (IoT) networks. In this paper, two RA deep network models are proposed for enabling a clustered underlay IoT deployment, where a group of IoT nodes are uploading information to a centralized gateway in their vicinity by reusing the communication channels of conventional cellular users. The RA problem is formulated as a two-dimensional matching problem, which can be expressed as a traditional linear sum assignment problem (LSAP). The two proposed models are based on the recurrent neural network (RNN). Specifically, we investigate the performance of two long short-term memory (LSTM) based architectures. The results show that the proposed techniques could be used as replacement of the well-known Hungarian algorithm for solving LSAPs due to its ability to find the solution for the problems with different sizes, high accuracy, and very fast execution time. Additionally, the results show that the obtained accuracy outperforms the state-of-the-art deep network techniques. [ABSTRACT FROM AUTHOR]

The application of network slicing to mobile edge computing (MEC) systems has aroused great interests from both academia and industry. However, the optimization of network slicing and MEC in most existing research works only focuses on resource slicing, energy scheduling, and power allocation from the perspective of mobile devices, without considering the operator's revenue. In this paper, we propose a novel framework for network slicing in MEC systems, including slice request admission and a revenue model, to investigate the operator's revenue escalation problem while considering traffic variations. The revenue model is mainly composed of the longer-term revenue and short-term revenue. Particularly, we jointly optimize slice request admission in the long-term and resource allocation in the short-term to maximize the operator's average revenue. By employing the Lyapunov optimization technique, we develop an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the DNSRA, to solve the problem. To reduce the computational complexity of directly solving the DNSRA, we decouple the optimization variables for efficient algorithm design. By this, the strategies for user association and CPU-cycle frequency are obtained in closed forms, respectively. Power allocation and subcarriers assignment are obtained by employing the successive convex approximation approach. Meanwhile, we develop a heuristic algorithm to obtain the dynamic slice request admission decision. Simulation results show that the proposed DNSRA can strike a flexible balance between the average revenue and the average delay, and can significantly increase the operator's revenue against existing schemes. [ABSTRACT FROM AUTHOR]

An efficient scheme for establishing a multicast route in packet networks is constructing a tree-shaped path in which multicast packets are replicated at specific branching nodes, called “copy nodes,” by the packet copy function and are forwarded to downstream nodes. This scheme leads to the efficient usage of network resources, but needs multicast routing control. To realize rapid multicast routing, it is suitable and realistic to restrict the number of copy nodes in calculating the multicast route. However, even in this approach we need to adequately select a limit on the number of copy nodes which satisfies the conditions of both priority for multicasting and geographical distribution. In this paper, a new copy-node allocation algorithm is proposed. This heuristic algorithm makes some “copy node functions” move on the network map sequentially according to attraction and repulsion determined by several conditions and finally makes them converge on the switching nodes suitable as copy nodes. In addition, the performance of this algorithm is evaluated by a computer simulation. This algorithm is applicable to various other server assignment problems. © 1999 Scripta Technica, Electron Comm Jpn Pt 1, 83(1): 87–96, 2000 [ABSTRACT FROM AUTHOR]

In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush–Kuhn–Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A progressive paradigm shift from centralized to distributed network architectures has been consolidated since the 4G communication standard, calling for novel decision-making mechanisms with distributed control to operate at the network edge. This situation implies that each base station (BS) must manage resources independently to meet the quality of service (QoS) of existing human-type communication devices (HTC), as well as the emerging machine type communication (MTC) devices from the internet of things (IoT). In this paper, we address the BS assignment problem, whose aim is to determine the most appropriate serving BS to each mobile device. This problem is formulated as an optimization problem for maximizing the system throughput and imposing constraints on the air interface and backhaul resources. The assignment problem is challenging to solve, so we present a simple yet valid reformulation of the original problem while using dual decomposition theory. Subsequently, we propose a distributed price-based BS assignment algorithm that performs at each BS the assignment process, where a novel pricing update scheme is presented. The simulation results show that our proposed solution outperforms traditional maximum signal to interference plus noise ratio (Max-SINR) and minimum path-loss (Min-PL) approaches in terms of system throughput. [ABSTRACT FROM AUTHOR]

In this paper, a framework is proposed to improve the effectiveness of the long-term-evolution vehicle-to-vehicle (LTE-V2V) communication system, where the uplink channel of the cellular user equipment (CUE) is reused by multiple V2V links. Since the orthogonal channels occupied by each CUEs are multiplexed by multiple V2V links, the serious interference is generated in the co-tier layer and cross-tier layer. In order to minimize the co-channel interference between CUE link and V2V links, an optimized resource sharing scheme is proposed. Firstly, Fuzzy C-means (FCM) algorithm is proposed to perform the clustering based on the geographical location characteristics of V2V links. In the FCM algorithm, the adjacent V2V links are grouped into a cluster to facilitate the solution of the channel allocation problem. Assuring the V2V links in the same cluster do not share the same channel, the adjacent V2V links are avoided to reuse the same channel. On this basis, the use of reasonable channel allocation scheme can effectively reduce the co-channel interference. Considering that the channel state information (CSI) of mobile links is slowly faded, the total system capacity is maximized by leveraging the CSI while the reliability can be guaranteed for all V2V links. The outage probability constraint is used to ensure the reliability of all V2V links. The channel assignment problem of V2V links is formulated as a weighted two-dimensional matching problem, and the interference bipartite graph is constructed. Finally, Kuhn Munkras (KM) algorithm is introduced to allocate the uplink orthogonal channel of CUEs for the V2V links in the same class in order to maximize the total system capacity. Numerical simulation results show that the algorithm can improve the total system capacity. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising paradigm, which brings computation resources in proximity to mobile devices and allows the tasks of mobile devices to be offloaded to MEC servers with low latency. The joint problem of cooperative computation task offloading and resource allocation is a challenging issue. The joint problem of cooperative computation task offloading scheme and resource assignment in MEC is investigated in this paper, where the vertical cooperation among mobile devices, mobile edge server nodes and mobile cloud server nodes is considered, and the horizontal computation cooperation between edge nodes is considered as well. A computation offloading decision, cooperative selection, power allocation and CPU cycle frequency assignment problem is formulated. The objective is to minimize the latency while guaranteeing the constraint of transmission power, energy consumption and CPU cycle frequency. The formulated latency optimization problem is a nonconvex mixed-integer problem in general, which has binary variables and continuous variables. In order to solve the formulated problem. A joint iterative algorithm based on the Lagrangian dual decomposition, ShengJin Formula method, and monotonic optimization method is proposed. The CPU cycle frequence allocation is handled by the ShengJin Formula method due to the cubic equation of one variable about the CPU frequence allocation. The transmission power assignment is handled by the monotonic optimization method. In the algorithm convergence with different number of tasks, the proposed algorithm can quickly and effectively reach the convergence state and getting the minimum task execution delay. Numerical results demonstrate that the proposed algorithm outperforms the Full MEC, Full Local and Full Cloud three schemes in terms of execution latency. [ABSTRACT FROM AUTHOR]

In this paper we study the nonlinear resource allocation problem, defined as the minimization of a convex function over one convex constraint and bounded integer variables. This problem is encountered in a variety of applications, including capacity planning in manufacturing and computer networks, production planning, capital budgeting, and stratified sampling. Despite its importance to these and other applications, the nonlinear resource allocation problem has received little attention in the literature. Therefore, we develop a branch-and-bound algorithm to solve this class of problems. First we present a general framework for solving the continuous-variable problem. Then we use this framework as the basis for our branch-and-bound method. We also develop reoptimization procedures and a heuristic that significantly improve the performance of the branch-and-bound algorithm. In addition, we show how the algorithm can be modified to solve nonconvex problems so that a concave objective function can be handled. The general algorithm is specialized for the applications mentioned above and computational results are reported for problems with up to 200 integer variables. A computational comparison with a 0, 1 linearization approach is also provided. [ABSTRACT FROM AUTHOR]

Summary: LEO satellites are growing rapidly in number, but also in configurability of the payload, which presents new challenges of how to allocate the satellite's resources. We present an algorithm to solve two subproblems of the resource allocation problem for multi‐beam satellites in large LEO constellations. Our approach produces a static beam placement and a static frequency plan, taking into account the user demands and interference conditions, respectively. We provide an easy to implement and fast approximation for the beam placement problem, which is converted into an Edge Clique Cover problem using graph theory. The frequency assignment problem is translated into a constraint satisfaction problem, which is later solved with a first‐fit heuristic. We apply our algorithm to a scenario based on SpaceX's Starlink constellation, and when compared to two different baselines, we show that our method requires 40% fewer beams while being able to provide service to 2% more users. [ABSTRACT FROM AUTHOR]

This article investigates the downlink communications of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. To maximize the system throughput, we formulate a joint optimization problem over the channel assignment, decoding order of NOMA users, power allocation, and reflection coefficients. The formulated problem is proved to be NP-hard. To tackle this problem, a three-step novel resource allocation algorithm is proposed. Firstly, the channel assignment problem is solved by a many-to-one matching algorithm. Secondly, by considering the IRS reflection coefficients design, a low-complexity decoding order optimization algorithm is proposed. Thirdly, given a channel assignment and decoding order, a joint optimization algorithm is proposed for solving the joint power allocation and reflection coefficient design problem. Numerical results illustrate that: i) with the aid of IRS, the proposed IRS-NOMA system outperforms the conventional NOMA system without the IRS in terms of system throughput; ii) the proposed IRS-NOMA system achieves higher system throughput than the IRS assisted orthogonal multiple access (IRS-OMA) systems; iii) simulation results show that the performance gains of the IRS-NOMA and the IRS-OMA systems can be enhanced via carefully choosing the location of the IRS. [ABSTRACT FROM AUTHOR]

In a cellular network (CN), cellular users (CUs) located nearby machine type communications (MTC) devices (MTC-Ds) may act as uplink gateways to relay data to the base station (BS). We consider non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC) at the receiver to increase the number of connected devices and the spectrum efficiency. Both underlay and overlay spectrum access modes (SAMs) are considered. We introduce a dynamic user grouping (UG) concept as a practical constraint for SIC, in order to decrease the receiver complexity. Moreover, SIC constraints are introduced based on a minimum signal-to-noise-plus-interference ratio (SINR) at the receiver. We formulate a joint dynamic UG, power allocation, and resource block (RB) assignment problem, aiming at maximizing the total sum-rate of both CUs and MTC-Ds. We transform the obtained non-convex mixed-integer programming problem into a convex problem by using a quadratic fractional programming (FP). A heuristic method is also proposed to reduce complexity. Simulation results demonstrate that the proposed solution outperforms the conventional CN (C-CN) method and the results of the proposed optimization methods are close to optimal solution obtained by the exhaustive search (ES) algorithm in terms of total average sum-rate and network connectivity, while requiring less transmit power. The heuristic method decrease the computational complexity of the FP method at the expense of a small reduction in spectrum efficiency. [ABSTRACT FROM AUTHOR]